A validation dataset is a sample of data held back from training your model that is used to give an estimate of model skill while tuning model’s hyperparameters.
The validation dataset is different from the test dataset that is also held back from the training of the model, but is instead used to give an unbiased estimate of the skill of the final tuned model when comparing or selecting between final models.
There is much confusion in applied machine learning about what a validation dataset is exactly and how it differs from a test dataset.
In this post, you will discover clear definitions for train, test, and validation datasets and how to use each in your own machine learning projects.
After reading this post, you will know:
How experts in the field of machine learning define train, test, and validation datasets.
The difference between validation and test datasets in practice.
Procedures that you can use to make the best use of validation and test datasets when evaluating your models.
Let’s get started.
What is the Difference Between Test and Validation Datasets? Photo by veddderman, some rights reserved.
Tutorial Overview
This tutorial is divided into 4 parts; they are:
What is a Validation Dataset by the Experts?
Definitions of Train, Validation, and Test Datasets
Validation Dataset is Not Enough
Validation and Test Datasets Disappear
What is a Validation Dataset by the Experts?
I find it useful to see exactly how datasets are described by the practitioners and experts.
In this section, we will take a look at how the train, test, and validation datasets are defined and how they differ according to some of the top machine learning texts and references.
Generally, the term “validation set” is used interchangeably with the term “test set” and refers to a sample of the dataset held back from training the model.
The evaluation of a model skill on the training dataset would result in a biased score. Therefore the model is evaluated on the held-out sample to give an unbiased estimate of model skill. This is typically called a train-test split approach to algorithm evaluation.
Suppose that we would like to estimate the test error associated with fitting a particular statistical learning method on a set of observations. The validation set approach […] is a very simple strategy for this task. It involves randomly dividing the available set of observations into two parts, a training set and a validation set or hold-out set. The model is fit on the training set, and the fitted model is used to predict the responses for the observations in the validation set. The resulting validation set error rate — typically assessed using MSE in the case of a quantitative response—provides an estimate of the test error rate.
We can see the interchangeableness directly in Kuhn and Johnson’s excellent text “Applied Predictive Modeling”. In this example, they are clear to point out that the final model evaluation must be performed on a held out dataset that has not been used prior, either for training the model or tuning the model parameters.
Ideally, the model should be evaluated on samples that were not used to build or fine-tune the model, so that they provide an unbiased sense of model effectiveness. When a large amount of data is at hand, a set of samples can be set aside to evaluate the final model. The “training” data set is the general term for the samples used to create the model, while the “test” or “validation” data set is used to qualify performance.
Perhaps traditionally the dataset used to evaluate the final model performance is called the “test set”. The importance of keeping the test set completely separate is reiterated by Russell and Norvig in their seminal AI textbook. They refer to using information from the test set in any way as “peeking”. They suggest locking the test set away completely until all model tuning is complete.
Peeking is a consequence of using test-set performance to both choose a hypothesis and evaluate it. The way to avoid this is to really hold the test set out—lock it away until you are completely done with learning and simply wish to obtain an independent evaluation of the final hypothesis. (And then, if you don’t like the results … you have to obtain, and lock away, a completely new test set if you want to go back and find a better hypothesis.)
Importantly, Russell and Norvig comment that the training dataset used to fit the model can be further split into a training set and a validation set, and that it is this subset of the training dataset, called the validation set, that can be used to get an early estimate of the skill of the model.
If the test set is locked away, but you still want to measure performance on unseen data as a way of selecting a good hypothesis, then divide the available data (without the test set) into a training set and a validation set.
This definition of validation set is corroborated by other seminal texts in the field. A good (and older) example is the glossary of terms in Ripley’s book “Pattern Recognition and Neural Networks.” Specifically, training, validation, and test sets are defined as follows:
– Training set: A set of examples used for learning, that is to fit the parameters of the classifier.
– Validation set: A set of examples used to tune the parameters of a classifier, for example to choose the number of hidden units in a neural network.
– Test set: A set of examples used only to assess the performance of a fully-specified classifier.
These are the recommended definitions and usages of the terms.
A good example that these definitions are canonical is their reiteration in the famous Neural Network FAQ. In addition to reiterating Ripley’s glossary definitions, it goes on to discuss the common misuse of the terms “test set” and “validation set” in applied machine learning.
The literature on machine learning often reverses the meaning of “validation” and “test” sets. This is the most blatant example of the terminological confusion that pervades artificial intelligence research.
The crucial point is that a test set, by the standard definition in the NN [neural net] literature, is never used to choose among two or more networks, so that the error on the test set provides an unbiased estimate of the generalization error (assuming that the test set is representative of the population, etc.).
Do you know of any other clear definitions or usages of these terms, e.g. quotes in papers or textbook?
Please let me know in the comments below.
Definitions of Train, Validation, and Test Datasets
To reiterate the findings from researching the experts above, this section provides unambiguous definitions of the three terms.
Training Dataset: The sample of data used to fit the model.
Validation Dataset: The sample of data used to provide an unbiased evaluation of a model fit on the training dataset while tuning model hyperparameters. The evaluation becomes more biased as skill on the validation dataset is incorporated into the model configuration.
Test Dataset: The sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.
We can make this concrete with a pseudocode sketch:
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# split data
data = ...
train, validation, test = split(data)
# tune model hyperparameters
parameters = ...
for params in parameters:
model = fit(train, params)
skill = evaluate(model, validation)
# evaluate final model for comparison with other models
model = fit(train)
skill = evaluate(model, test)
Below are some additional clarifying notes:
The validation dataset may also play a role in other forms of model preparation, such as feature selection.
The final model could be fit on the aggregate of the training and validation datasets.
Are these definitions clear to you for your use case?
If not, please ask questions below.
Validation Dataset Is Not Enough
There are other ways of calculating an unbiased, (or progressively more biased in the case of the validation dataset) estimate of model skill on unseen data.
One popular example is to use k-fold cross-validation to tune model hyperparameters instead of a separate validation dataset.
In their book, Kuhn and Johnson have a section titled “Data Splitting Recommendations” in which they layout the limitations of using a sole “test set” (or validation set):
As previously discussed, there is a strong technical case to be made against a single, independent test set:
– A test set is a single evaluation of the model and has limited ability to characterize the uncertainty in the results.
– Proportionally large test sets divide the data in a way that increases bias in the performance estimates.
– With small sample sizes:
– The model may need every possible data point to adequately determine model values.
– The uncertainty of the test set can be considerably large to the point where different test sets may produce very different results.
– Resampling methods can produce reasonable predictions of how well the model will perform on future samples.
They go on to make a recommendation for small sample sizes of using 10-fold cross validation in general because of the desirable low bias and variance properties of the performance estimate. They recommend the bootstrap method in the case of comparing model performance because of the low variance in the performance estimate.
For larger sample sizes, they again recommend a 10-fold cross-validation approach, in general.
Validation and Test Datasets Disappear
It is more than likely that you will not see references to training, validation, and test datasets in modern applied machine learning.
Reference to a “validation dataset” disappears if the practitioner is choosing to tune model hyperparameters using k-fold cross-validation with the training dataset.
We can make this concrete with a pseudocode sketch as follows:
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# split data
data = ...
train, test = split(data)
# tune model hyperparameters
parameters = ...
k = ...
for params in parameters:
skills = list()
for i in k:
fold_train, fold_val = cv_split(i, k, train)
model = fit(fold_train, params)
skill_estimate = evaluate(model, fold_val)
skills.append(skill_estimate)
skill = summarize(skills)
# evaluate final model for comparison with other models
model = fit(train)
skill = evaluate(model, test)
Reference to the “test dataset” too may disappear if the cross-validation of model hyperparameters using the training dataset is nested within a broader cross-validation of the model.
Ultimately, all you are left with is a sample of data from the domain which we may rightly continue to refer to as the training dataset.
Further Reading
This section provides more resources on the topic if you are looking go deeper.
Do you know of any other good resources on this topic? Let me know in the comments below.
Summary
In this tutorial, you discovered that there is much confusion around the terms “validation dataset” and “test dataset” and how you can navigate these terms correctly when evaluating the skill of your own machine learning models.
Specifically, you learned:
That there is clear precedent for what “training dataset,” “validation dataset,” and “test dataset” refer to when evaluating models.
That the “validation dataset” is predominately used to describe the evaluation of models when tuning hyperparameters and data preparation, and the “test dataset” is predominately used to describe the evaluation of a final tuned model when comparing it to other final models.
That the notions of “validation dataset” and “test dataset” may disappear when adopting alternate resampling methods like k-fold cross validation, especially when the resampling methods are nested.
Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.
Hey nice article! I’m a fan of your posts here but whenever I try to print these articles to carry with me on the metro to read there are these big floating advertisements for your machine learning tutorials that basically make it unreadable 🙁
I printed an another articles of you. And I came with the same problem as Jacob Sanders mentioned. Every page has a Ad (“Get your start in machine learning”) which cover a large space resulting in unreadability.
Hence, I can only read your articles online and cannot print it.
What a pity! : (
If you want to print it, you need to convert the URL first to PDF (e.g. webpagetopdf.com). Trying to print the page directly is scattered with Jason’s opt-in. But that’s not really a problem. For that great (and free) content!
We should split data for training,validation,testing,but what is the best way to split? It is best to make the three data set as homogeneous as possible.
What do we call the set of data on which the final model is run in the field to get answers — this is not labeled data. How do we evaluate the performance of the final model in the field?
We don’t need to evaluate the performance of the final model (unless as an ongoing maintenance task).
Generally, we use train/test splits to estimate the skill of the final model. We do this robustly so that the estimate is as accurate as we can make it – to help choose between models and model configs. We use the estimate to know how good our final model is.
Hi thank you for nice article. I want to check the model to see if the model is fair and unbiased but my professor told me with cross validation or 10-fold cross validation or any of this methods we can’t confirm if the model is valid and fair. can you please give me some hints about which method I can use for this problem?
Thanks
Hi, Jason, greate article as always. I think Helen’s question is that her professor told her k-fold method can not cofirm if the model is valid. My understanding is that the test data from k-fold method actually come from the sample data with training data.
I have encountered the same problem myself. if I sample a subset as test data from original data, and the rest as training and validation data. model can perform well on three data set. But work not well on new data(not included in the original data at all). I think the way of split the test data is important. Should not use the sample method.
Studies have shown that using cross-validation with a well-set k value gives a less optimistic estimate of model performance on unseen data than a single train/test split. At least on average.
Yes, I think on average it would helped. I worked on time-series data and it performed not well. That’s why I try to figure out the reason. The following article validates my thought, this should express the problem I encountered more clearly 🙂
I think that is because the k-fold method means your test data are sampled from the same source with the training data. so it can not completely prove the the validation of the model.
I learnt a method called rolling windows in a course I did, it represents cross validation specifically for time series data, after the data set is coerced to be indexed by time column. Then tagged/labelled a periodically (monthly, quarterly, bi-annually, annually) depending on data size, and if spanning years merged with year (YEAR+PeriodIndex = 2020Q1, 2020Q2 etc…This reps the dummy for each observation that represents which time segment that observation falls into. Similarly to k-fold cross validation techniques, the model is trained on 1- 4 Qtr periods and predicts on the 5th quarter (in my use case).
Helen, we usually process our features or variables across the entire training dataset. ie for instance when we standardize our continuous features – we take the Mu and the Sigma across the entire training set, and not on each fold of the CV. Similarly, there are times when we do some form of missing data imputation which also looks at the entire training data, and not each fold.
Meaning, when we do 5 fold or 10 fold CV, even though we explicitly train only on 4 (or 9) folds of the training data, there’s some learning which is gotten indirectly from the data that we think the model hasn’t seen
This could be a reason why your professor says this isn’t a good way to choose a final model and we will necessarily have to use hold out data
Nice article, really helped me to refresh my memories.
One little note: In your first code example you loop over parameters but you never use params in the loop’s body. I guess it should be used in model = fit(train, params)!?
in the pseudocode of the part “Validation and Test Datasets Disappear”, I still didn’t understand how you used k-fold cross-validation to tune model hyperparameters with the training dataset.
Want to make sure my understanding is correct. If not, please correct me.
In general, for train-test data approach, the process is to split a given data set into 70% train data set and 30% test data set (ideally). In the training phase, we fit the model on the training data. And now to evaluate the model (i.e., to check how well the model is able to predict on unseen data), we run the model against the test data and get the predicted results. Since we already know what the expected results are, we compare/evaluate predicted and expected results to get the accuracy of the model.
If the accuracy is not up to the desired level, we repeat the above process (i.e., train the model, test, compare, train the mode, test, compare, …) until the desired accuracy is achieved.
But in this approach, we are indirectly using the test data to improve our model. So the idea of evaluating the model on unseen data is not achieved in the first place. Therefore ‘validation data set’ comes into picture and we follow the below approach.
Train the model, run the model against validation data set, compare/evaluate the output results. Repeat until a desired accuracy is achieved.
Once the desired accuracy is achieved, take the model and run it against the test data set and compare/evaluate the output results to get the accuracy.
If this accuracy meets the desired level, the model is used for production. If not, we repeat the training process but this time we obtain a new test data instead.
It is a balancing act of not using too much influence from the “test set” to ensure we can get a final unbiased (less biased or semi-objective) estimate of model skill on unseen data.
Thank you for this article. I have a question, though. I’m new to ML and have been working on a case study on credit risk currently. My data is already divided into three different sets, each for train, validation, and test. I would start by cleaning the train data (fining NA values, removing outliers in case of a continuous dependent variable). Do I need to clean validation and test datasets before I proceed with the method given above for checking the model accuracy? Any help would be really appreciated. Thanks.
I have a question concerning Krish’s comment, in particular this part:
“If the accuracy is not up to the desired level, we repeat the above process (i.e., train the model, test, compare, train the mode, test, compare, …) until the desired accuracy is achieved.
But in this approach, we are indirectly using the test data to improve our model. So the idea of evaluating the model on unseen data is not achieved in the first place. Therefore ‘validation data set’ comes into picture and we follow the below approach.”
I’m working on a model using sklearn in python. Given that I’m training on the train dataset with some parameters, and testing on the test dataset and then reexecuting the python file but with different model parameters for training to choose between parameters, I was wondering in what this approach is different than to iteratively train a model with different parameters, evaluate it on a validation set and test it on a test set.
And does the same go for the validation set: The more iterations of train-validation we make, then the more we will be tuning parameters to the noise in the validation set (leaking data?)? Although if it is too much then presumably it will do worse on the test set anyway, ie moved from underfitting to overfitting?
Would one way to reduce the need for iteratively tuning parameters, via cycles of train-validation, be to better understand the problem, its data and the available modelling tools / algos in the first place?
To iterate on what was said/asked above by Krish (Similar question), when I learnt ml, I learnt to first take the whole data set and split it 80% (Train) / 20% (Test) (test_train_split method with 80/20 params indicated). This is a completely random (so Im assuming the index is shuffled). Then of the 80% training data, it was further split it into 5 (fold) divisions, of which 1 of 5 will rep another test set (secondary test set), and the CV procedure iterates so that every 1 of 5 subdivisions gets used as the secondary test set. Isn’t this hold out secondary test set essentially the validation set? Why does the article state that the need for a validation set disappears when K-fold CV is used? This prediction on the secondary test set of the 80% training set, is part of the evaluation of the training model, before the final prediction on unforeseen data the original 20% test set.
For large datasets, you could split e.g. 95% vs. 5% (instead of 70%30%).
Reasons: you give your neural network enough data to train. 5% on a large dataset is still big for dev and/or test. Andrew Ng calls this the new way of DL splitting the data.
Again a good overview. However I want to point out one problem when dividing data into these sets. If the distributions between the data sets differ in some meaningful way, the result from the test set may not be optimal. That is, one should try to have similar distributions for all sets, with similar max and min values. If not, the model may saturate or underperform for some values. I have experienced this myself, even with normalised input data. On the other hand, when dealing with multivariate data sets, this is not easy. It would be nice to read a post on this.
I believe you are saying:
“First split into train#1/test then split the train#1 into train#2/validation, this last train#2 set is used for CV.”
However, based on the following comment in your article, I thought with CV you didn’t need a validation set:
‘Reference to a “validation dataset” disappears if the practitioner is choosing to tune model hyperparameters using k-fold cross-validation with the training dataset.’
Please clarify.
BTW: I really appreciate these well-done articles. Thank you.
if I want to split data to train/test (90:10), and I want to split train again to train/validation, train/validation (90:10) right or I can split with free ratio ?
– The theory of train/test is, that you tend to overfit if you optimize on test set.
– The theory of train/dev/test is, that you optimize train/dev and only see if it works with test.
I don’t know who came up with train/test/dev. Maybe Andrew Ng? Intuitively, I like train/dev/test.
With practitioners, I don’t see it being used.
To me, Andrew Ng doesn’t count as a practitioner.
Does Jason use it? I have not seen it.
Does Francois Chollet (creator of Keras) use it? Not that I’m aware of.
Hi Jason,
I just wish to appreciate you for the very nice explanation. I am clear on the terms.
But I would like you explain more to me on the tuning of model hyperparameter stuff.
Thank you for this post. It’s amazing, I have cleaned my misunderstood of the validation and test sets.
I have a doubt maybe it is out of this context but I think it is connected.
I want to plot training errors and test errors graphical to see the behavior of the two curves to determine the best parameters of a neural network, but I don’t understand where and how to extract the scores to plot.
I have already read your post: How to Implement the Backpropagation Algorithm From Scratch In Python, and learned there how to have the scores using accuracy, (I am very thankfully for that).
But now I want to plot the training and test errors in one graphic to see the behavior of the two curves, my problem is I don’t have an idea of where and how to extract this errors (I think training errors I can extract on the training process using MSE), and where and how can I extract the test errors to plot?
Please help me with this if you can. I am still developing a backpropagation algorithm from scratch and evaluate using k-fold cross-validation (that I learner your posts).
I have already tried to do so, but there are two problems I find on my self:
The first problem is (my predict function returns 0 or 1 each time I call it in the loop, with this values I can calculate the error rate and the accuracy), my predict function uses the forward function that returns the outputs of output layer that are rounded to 0 or 1, so I am getting confuse If I have to calculate this errors using these outputs from the forward function inside the predict function before round to 0 or 1 (output-expected)? or I will calculate these errors inside the k-foldCV function after the prediction using the rounded values 0 or 1 (predictions – expected)?
The second problem is (In the chart of training error I plotted using a function of training errors and the epochs. But here in the test error I can’t imagine the graphic will be a function of the errors with what values since I need to plot this errors in the same graphic).
My goal is to find the best point (the needed number of epochs) to stop training the neural network by seeing the training errors beside the test errors. I am using accuracy but even I increase or decrease the number of epochs I cant see the effect in the accuracy, so I need to see these errors side by side to decide the number epochs needed to train to avoid over fitting or under fitting.
I am really stuck at this point, trying to find a way out everyday. And here is where I find most of solutions of my doubts in practice.
Perhaps try training multiple parallel models stopped at roughly the same time and combine their predictions in an ensemble to result in a more robust result?
Hi;
Thanks for your beautiful explanation. I think one reason for such a confusion among many people about training, test and validation datasets is the fact that depending on different steps of data analysis we have to use the same terms for these datasets, however, these datasets will be changed and are not the same. For example, let’s say there is an analyst who wants to predict “y” from several “x”s using the Naive Bayes Classifier. S/he is going to use a training dataset and a test dataset. After prediction of “y” then s/he want to validate the model. In this case the previous test dataset may act as a validation dataset after partitioning it into training and test datasets. So, we are using validation and test terms almost equal, but depending on what is the purpose of analysis it may different based on predicting our dependent variable (using training and test datasets) or just for assessment of model performance using previous test dataset(=validation) and partitioning into training and test dataset.
I got to Chapter 19 in your Machine Learning Mastery with Python book and needed more explanation of a validation dataset. Google led me here and now I realize what a great complement your site info is to your books for deeper insight into some topics and/or expanded explanations. I’m really enjoying learning through your books.
Hello Jason! It was a really good post which helped me understand the differences between the terms.
But I have a small problem. Recently I sent a manuscript to a journal and I carried out the following steps to develop the model
1) Split the Dataset into training(80%) and testing(20%)
2) Performed 5 fold CV on the training dataset to choose model hyperparameters i.e the best architecture for a DNN and then trained the model with complete training dataset
3) While training the model ie. backpropagation step I also used early stopping. I used a new dataset ( other than training and testing) to determine the maximum epochs at backprop step.
4) Finally after developing the DNN completely I tested its generalization on the test dataset which was never used before.
The reviewer said that generally ML practitioners split the data in Train, Validation and Test sets and is asking me how have I split the data?
I think my approach is good and I have written everything clearly. How do I explain that there is no need to choose a validation set when you are applying k fold CV? I dont want to blow this up since there’s only once chance to communicate with the reviewer.
Thanks for replying. But during k fold cross validation we do not explicitly take a validation set. We divide the training data into k subsets and repeat the training procedure k times each time using a different subset as a validation set. Then we average out the k RMSE’s and get the optimal architecture. Once we get the optimal architecture we train on the complete training dataset. Right?
I have to compare the two strategies to choose optimal parameters of my NN. I have the following reasoning. Please tell if it is correct!
In train-validation-test split case, the validation dataset is being used to select model hyper-parameters and not to train the model hence reducing the training data might lead to higher bias. Also the validation error deoends a lot on which data points end up in the validation set and hence different validation sets might give different optimal parameters i.e. the evaluation results in higher variance.
In k fold cv , which is a more progressive procedure, each subset and hence every data point is used for validation exactly once. Since the RMSE is averaged over k subsets, the evaluation is less sensitive to the partitioning of data and variance of the resulting estimate is significantly reduced. Also since all the data points are used for training bias is also reduced.
You can choose to evaluate a model any way you wish. There are no ideas of correct. What matters is how the model actually really performs when used on new data. All these methods are an attempt to estimate this unknown quantity.
I have thousands of road camera images that captured by the same camera for every 5 min time intervals. So my questions is that as it is being kind of time-series problem since image domain not much changing, should I need to use TimeSeriesSplit from sklearn for getting a trustable result, or do you suggest anything for me on this?
I am little confused I can’t find enough explanation when it comes to images because generally discussions are being done around numerical data splitting. Would you think to write something on images, too?
I am trying to compare two different sets of data that are millions of lines in size. One set is approximately 10% bigger than the other so in looking over the explanations presented, as well as the other links, I am not sure the K-fold perspective would be appropriate.
Is there a k fold usage that would allow for determining if the data sets are the same and being able to define why the difference would occur?
Hi Sir. I have a problem. If we want to use random forest with cross validation, then the result in R show us there are accuracy. How if we want to use ROC curve for the accuracy of every fold of random forest? Thanks
Quick question: after determining the best model (fit with best hyperparms through training, with k-fold cross validation, then test predict using out-of-the-box testing data set), do we still have to do another fit() against the entire data set to finalize the model with the given hyperparms? I have always assumed that the entire set needs to be fitted one last time before release to the wild.
Note, you can pick and choose a methodology that is right for your problem. The post above is trying to present a general approach to cover many problem types.
I am still confused on how the workflow when you want to show how multiple models compare and then perform hyper-parameter tuning on the best one.
Lets say my objective is to perform supervised learning on a binary classifier by comparing 3 algorithms and then further optimizing the best one.
Workflow:
1. Separate train and test data.
2. Perform model fitting with the training data using the 3 different algorithms. Evaluate and compare the performance of each against the test data (like ROC AUC).
3. Take the best model from step 2 and perform hyper-parameter tuning with only the training data, evaluating and reporting the performance against the same test data.
My spidey sense tells me I shouldn’t be making decisions off of my test data results in step 2, but how else can I compare them? Does cross-validation really buy me anything here?
Hi Jason,
Thank you for the article. I am working on a problem where the training set and test set are provided separately. I am following similar approaches mentioned in yours machine learning mastery books. First, I divide the training set into train and validation sets. Then, I build the models on train set using 5-fold cross validation. I achieved the accuracy of around 99% (+/- .003%) for machine learning classifiers such as Decision Tree, Random Forest, and Extra Tree. When I apply these models on validation set, I get almost the same accuracy. Finally, I apply these models on the separately provided test set. However, the models do not give me the similar results. Is this a case of over-fitting?
Hi Jason, thank you for your nice article. As i’m new to ML i’m still a bit confused. I am doing a binary classification on a data set about 50000 data using different ML algorithms. I manually divide the data to train and test (using 80% for training). using sklearn library, applied different classifiers without any tuning and got almost well results. Could my work be completely wrong or useless because I didn’t have a validation set and tuning? I mean is it a must?
And which performance metric is the best metric to be considered in the comparison of different algorithms? If i’m not wrong, the accuracy could not be a good metric to be used alone except that the data is balanced. so, for comparison should I consider all the metrics such as accuracy, precision,…,together? or for example just comparing the f1-score?
I understand while building machine learning models, one uses training and validation datasets to tune the parameters and hyperparameters of the model and the testing dataset to gauge the performance of the model.
But given that in most cases, these ideal (tuned) values change with the size of the dataset, the ideal parameter and hyperparameter values are going to be different for a production training dataset. So, how does one tune these values when going to production where there is only a training dataset?
Possible solutions:
Divide the production dataset into training and validations sets. This can be done is some cases but in many cases, it is desirable to train the model on all the data available, especially in time series problems where the latest datapoints may be more valuable. Even otherwise, most models perform better with more data (except the ones that don’t). So this can’t be solution in all cases.
Train on multiple sizes of training datasets and establish the relationship between the training size and change in ideal para-hyperpara values. Then, apply this relationship while setting up the production model.
The second one seems a better solution but in itself, becomes a considerably difficult problem for which, one might use another machine learning model. Are there any existing frameworks for doing this process? (Preferable in python or tensorflow)
I took 20% off my data as a test set (stratified sampling, so the ratios of my two classes stay alike). On the 80% I conduct 10-fold CV, having 4 different classifiers to compare. This gives me 40 different models/ different sets of parameters.
I can compare these models now based on calssification metrics and can even define my “best approach”, for example taking the one with the highest average f1-score over 10 folds.
Now the following step is unclear and I just can’t find a reference in literature I could stick to (or I could quote from for my academic work): What model do I use now to get my unbiased estimate from the test set?
There are two possibilities:
1. Chose the best classifier (based on the average f1score), retrain it on the whole 80% and the use this model for the test set.
2. Chose the best classifier (based on the average f1score) and take the model in the “best” fold (highest f1score reached), use it on the test set.
Also, I also think it is interesting how the other 3 classifiers perform on the test data (either chosing the model of the “best fold” of every classifier or by retraining every classifier on the 80%), but I somehow fear that I devalue the results of the CV if I also monitor their performance on the test set. I know that under no circumstances, the test set should be used to SELECT between the models, but I think having an unbiased estimate for each model is also interesting.
Could you please comment my problem, maybe even with a literature reference? Thank you very much!
My suggestion would be: You find the best model config using CV (discard all the models), then re-fit a new model on all training data and evaluate it on test.
Dear Jason,
I fitted a random forest to my training dataset and prediction was very good. The predicted pattern almost exactly followed the actual value. But no so with validation data. RMS errors was 5 to 6 times compared to RMSE for training data
I tried tuning the parameters of RF and even changed the features that are inputs to model
that did not help either
what could b the reasons.
The training and validate dataset are 70/30% split of original dataset
Thanks
Thanks for the excellent tutorial Jason. I have a question. I am doing MNIST training using 60,000 samples and using the ‘test set’ of 10,000 samples as validation data. So in every epoch I train on 60,000 and then evaluate on 10,000. Then I report the best evaluation accuracy across all epochs. My question is – is this approach wrong?
Is the kfold method similar to a walk forward optimization.?..I come from a trading background with no knowledge of programing but use software package that has a wall forward optimizer.
Your explanation on training data sets, validation data sets and test data sets is highly educating. In view of these explanation, how do we now differentiate between validation and testing? Can we conclude that validation takes place on the process of development of a research package, while testing takes place after the package completion to ascertain for example functionality or ability to solve an educational problem for example.
Given the goal of developing a robust estimate of the performance of your model on new data, you can choose how to structure your test harness any way you wish.
Thanks for the article. However, suppose , I have a single data set with 50 thousand observations.(no test set is given separately), Can I divide the given dataset into 3 parts i.e train, validation and test and proceed with modelling.
Hi, Jason, really nice Article, like always,I am big fan of your blog, I am working on EEG of Bonn university, there is, I have 11500 observations and 178 features
is it correct if I first do train/test split with rang .20, then using this training as again with range .30 train/validate ?
since I am using keras, it during validation, I can probably play with epoch and batch size only to find good model, my question is that for should I also do parameter tuning extra with this training set and validation test and with this model
and at the end, should i try the result on test set ? if the result does not work, or it was over fitting , how can i improve it
Training Dataset: The sample of data used to fit the model.
•Validation Dataset: The sample of data used to provide an unbiased evaluation of a model fit on the training dataset while tuning model hyperparameters. The evaluation becomes more biased as skill on the validation dataset is incorporated into the model configuration.
•Test Dataset: The sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.
appreciate the explanation Jason, and might have a vague Idea on what you have said here, but still a little foggy. so, I have quote your summary of the three definitions above.
still a bit hung up on difference between validation and test. they both are “used to provide and unbiased evaluation of a model fit” one, though (the test) is a FINAL model fit. so, let me try to say this in laymans terms. the validation set it seems to me is kind of an intermediary part that “tweaks” the training set; its intention is to improve/fine tune it. so that if we had a 60, 25, 15 split, where the 25(the validation) improved on the 60 (the training), we believe that to be better than taking the whole 85 as a training and then testing with the last 15? is that correct? thanks john
Shall we do manual split of training validation and test set in different directories or through programming. In this case weights or samples get changed kindly advise the best practice
Test dataset: a general hold out dataset that you test every config. of hyper-param, after each K-fold validation, to have a standard test of the tuning ? is that right ?
You mean the split disappears to say that in the end of the model selection/tuning you do a fit with the entire dataset ?
I have a data set that is highly imbalanced e.g. 1,000,000 observations where only 100K obs are response=1 and I want to partition train/validate/test sets as 70/20/10.
If I balance the training set say 70K 1s & 70K 0s, do I need to
a) also balance the validation set or
b) the validation set should reflect the true proportion of the imbalanced classes?
Why the testing data are identical to training data? what is the main goal.
if the system is evaluated with the same training and testing dataset (100% for training and 100% for testing), but the obtained accuracy is less 100. what this means?
Hello Jason, have you posted an article that contains the code which uses a validation data set ? If yes could you please share the link ? and if not, could you share any other useful link which has the code that uses this validation dataset !!! It would be so helpful!!
Thank you very much for a very interesting read! I was hoping to hear your thoughts on the following. After using the training and validation set to choose the optimally tuned model, and after applying that optimally tuned model to the test set to get an unbiased estimate of the out-of-sample performance, would it make sense to re-estimate the model using the optimal settings using ALL the data (train + validate + test) to create the optimal model that can be applied for data that is ACTUALLY new (such as the next patient that will arrive tomorrow)? I don’t see any reason why you wouldn’t want to use the test data for training your model after you’ve obtained a good estimate of out-of-sample performance, but perhaps I am missing something. Would be really happy to hear your thoughts; thanks again!
Nice article! I have a doubt on testing data set. I have to apply the model for a new set of testing data, then how can I predict the unseen dataset. Is this future data set have been collected and analysis the procedure.? And how it can be validate the single customer using this procedure? So , Is we really need a set of new customer data..? Am I right?
I have a question about a strategy that is working very well for me. Please let me know if this makes sense.
I split my entire data into train,validation and test sets. Then I used k-fold cross validation with Gridsearch i.e. GridsearchCV to find the best set of hyperparameters that give me the least MSE score.
Now I evaluate the performance of the above down-selected model on the validation set find out the MSE on the validation set, tweak the hyper-parameters further(Say manually) to see if I can lower the MSE on the validations set.
Then I club the train and validation sets and train the model with parameters obtained from step-2 to make predictions on my test set.
Hi Jason, Thank you for the post..!
I have a question if we should do this partitioning only in predictive modeling or can it be done in descriptive modeling too? I know we make models in predictive and then test accuracy where in descriptive we look for some patterns but in this too, we use k-means clustering model.
So, partitioning in descriptive is strictly NO or not recommended or can we have it?
the 3 sets-partition seems to be a good idea and my intuition agree with it. But if u were to answer – what is the main reason to use not only test set, but also validation set ? Why it is better to tune parameters on the other set, when test set is also held-out form training sample and we use the same test set to every method we want to compare. If we would tune parameters on test set and compare the best models of every method, why would it be worse approach ?
As i mentioned, i can fell that is is really good idea and it makes sense, but i can’t name the reason to use it in practise instead of using just test set to evaluate models and to tune parameters.
We use a validation dataset to avoid overfitting the test set – e.g. too many tests against any dataset will result in a natural hill climbing of that dataset and overfitting.
I’ve split my raw data into 70/30 training/split and the I split my training again into 70/30 training/validation. I set the number of layers and threshold and then I train my model on this last mentioned training set.
What do I do know (in R Studio) with the validation set? Do I compute (i.e. predict), or do I recreate the NN but instead of using the training data, I use the validation data?
hai jason saya ada beberapa pertanyaan
1. Pada saat menerapkan model machine learning, umumnya dataset yang dimiliki dibagi menjadi dua, yaitu train data dan test data. Jelaskan mengapa harus ada pembagian dua jenis data tersebut? Dalam kasus lainnya, terkadang diperlukan juga validation data. Jelaskan apa maksud dari validation data dalam kaitannya dengan train dan test data? Bagaimana jika ada salah satu data yang tidak ada?
2. Jelaskan mengenai overfitting dan bagaimana cara yang dapat dilakukan untuk mengatasinya!
3. Jelaskan mengenai konsep dan cara kerja dari metode gradient descent (GD) untuk pelatihan model logistic regression (LR) secara lengkap! Mengapa metode GD dipastikan akan menemukan nilai bobot w yang paling baik untuk model LR?
One dataset is used to train the model and the other is used to evaluate the skill of the model. Validation can be used to tune the model hyperparameters prior to evaluating the model on the test set. We cannot use the train or test set for this purpose otherwise the evaluation would not be good, or valid.
It’s a good article to clarify some confusions.
I’m working on cancer signature identification using qPCR data. I’ve a training/validation set and a separate test set. After the identification of the best signature (a combination of some important features) using FS and k-CV on training/validation set, I want to determine the performance of the signature on the separate test set. Since the final aim is to deliver the best signature (set of variables) to be used in diagnosis in other clinics centers, which performance evaluation method is suitable for that among:
1- method1
model = fit(train) #train set is the projection of the train data on the features containing in the selected signature
skill=evaluate(model, test)
This is the classical method, i.e modeling on train data and testing on test data
2- method2
i- taking only the separate test data and the features containing in the selected signature
ii- splitting the test data into train_test and validation_test (multiple time), and take the mean performance as follows:
model = fit(train_test) #train_test set is the projection of the train data on the features containing in the selected signature
skill=evaluate(model, validation_test)
Here, we fit the model on test data and we test it on test data using multiple resampling
Suppose both of method1 and method2 give good evaluation results. If we use method1, for reusing of our signature in other clinical centers, we must deliver the reference dataset used to find the signature and the signature (variables). However with method2, we will able to deliver only the signature (i.e the variables) to be used in other centers, and this is our objective.
sir can you please tell me how to implement model if we have train and test datasets with dissimilar content and values then how to predict the test dataset of that new values..
example:in train dataset- {id,customerid,age,valid} valid is target
in test dataset{id,customerid,age} the values in this are different from the train dataset that is it is a new data for which we have to predict a valid column for this new data of testdataset
hello i have a seperate train and test data set. While cleaning the data by imputing missing values and outliers, should i clean both the train and test data.
Very nice article as always, I learned a lot. I’m using a weak/distant supervision method to generate some training data. So I don’t have a training data for now, meaning no training samples with gold labels. Although, I do have a limited number of labeled samples which I want to use as my dev and test sets. I have two questions:
1) My main question is how can I make sure my test set is representative of all the cases that may happen and my model would like to learn if I’m not supposed to look at the test set since our results may become biased. How can we know test set is covering all different samples that a machine should learn and is going to be evaluated on?
2) My second question, if I randomly select a dev set and evaluate the performance of model on the dev set, is it OK if I do error analysis on dev set (e.g. look at the actual false positives and false negatives) and then go back and refine my weak supervision method? Based on your article, I realize that it may make my model in bit biased, but I’m just wondering what the best thing to do is. Since my number of labeled samples is limited, I may not be able to every time create a new sample for my test set, so at least I want to make sure I’m training my model with the help of the dev set best as I can.
Even a short response would be of great help. Thanks a lot.
This is a great blog Jason. I appreciate your effor to put this together and patiently answer our questions. I have a basic confusion, here is my question. If it has already been answered, my apologies (I looked but could not clarify this)
So let’s say I have a dataset with 2000 samples, I do a 1000:500:500 split (training:validation: test). Say I set # of epochs to 30. At the end of each epoch, accuracy score is calculated by checking performance on validation data. Doesn’t that mean, the model is looking at the same validation data set multiple times?? Then what’s the point of validation holdout if the data is visited multiple times like training data?
Yes, the validation set will be evaluated at the end of each epoch – but it is not used to train the model, only to evaluate it objectively (somewhat).
Yes, it kind of does. But still, if the model is going over the validation data to calculate the evaluation score, does it impact reliability? (I understand that the parameters are not updated during evaluation) but the validation data is not ‘unseen’ after the first epoch. Thanks
Unless you use the scores a lot and you – yourself – the human operator – begin to overfit the validation set with the choice of experiments to run. This can happen.
Hi Jason
Is it right to split data in traing validation and test sets in 60%20%20%?fit the model than validate the model on validation set by getting best hyperparameters /hypertunning and stopped traing to best epoch to avoid obeefotirng and than applied the tuned model on test data for prediction?
Hi. Can I see final hypertunning paprameters? And I use simple validation for hypertunning than stopped training to 30 epochs when validation loss is less.
2nd I want to ask model.predict does the prediction work? as I saw in your examples too. 3rd binary cross entropy can be used for regression too or just for classification problem in CNN for time series data? I am confused about this. If you have link kindly share it. Thanks
I am using CNN for time series data of wind power. Comparing actual VS predicted and calculating mse as performance metric. But as you said before you can’t use cross entropy for regression problem. I am predicting power. So it’s ok to use cross entropy for predicting power definitely in numerical form or should use mean squared error as loss function in CNN.
I am using binary cross entropy as loss function for time series prediction of wind power and performance is based on mse. Not mean squared error and using pytorch in python.
Thank you very much Jason for this article, it’s a lifesaver.
For me it’s always better to deal with numbers, let’s say we have a 1000 data samples, from which 66% will be splitted into training and 33% for testing the final model, and am using a 10 cross validations, now my problem arises with the validation and the cross validation percentages.
What I understood is that we’ll build 10 models from the training data, each model uses (10%) from the training data (which is 0.1*66% of the total data set), and validate it using different 10% training data, from those 10 models we tune the final model’s parameters, and use the 33% testing data to get a final estimation of the model.
And in both Validation and Training, we got an unbiased estimation, did I get it right?
Hi Jason, in this article you speack of independant testing set. What does it mean ? Is it datas take from the same data set as the train data but that are different. Or does it mean that this independant testing set is made with data that are completly different from the training set ?
Ok thank you for your reply. I asked you this question because I work on a chatbot and my team wants to use the data revored by the chatbot only for the testing set and not for the training set (and generate the training data ourself). Does it make any sense ? Because from my point of view we should put the recovered data in the training and testing set.
nice article, i have just one doubt, i heard that we must use the test dataset to evalute the performance of two or more different models. But if we use the exactly same data for train and validate both models we could not eliminate the use of a test dataset ? Because both are using the same data, so they have the same chance to fit in the validation dataset, theoretically.
I am a bit puzzled by the nested CVs you mention towards the end (I have a small dataset so it is quite relevant to me not to leave out chuncks of data if possible):
1. assume you do 10-fold CV on the whole dataset
2. at each iteration you put away a fold for testing and you do CV on the remaining 9 to tune the model hyperparameters
However, for every iteration, in step 2 you will probably choose a different set of hyperparameters. Which then do you use for the final model? A natural answer is to choose the set with lowest test error, computed through CV on the whole data set. But then, why not to directly choose your hyperparameters through CV on the whole data set? (The latter is slightly unsatisfactory as it would mean validation \approx test, but I cannot see a way out, especially because it is likely that no two sets of hyperparameters are equal in step 2 above, so no majority voting either.)
You are in effect cross-validating the model configuration process. The result will indicate that: when you use “this” model with “this” tuning process the results are like “that” on average.
Nested CV might be the most robust approach at the moment for small datasets. I will write a dedicated post on the topic ASAP.
Hi, first of all, thanks, these posts are really helpful.
So I have a small dataset (around 1000 rows) for classification, and my doubt is about the process that I am following:
1) In the first place, I prepare the data.
a) I remove the columns that might be useless;
b) I use oneHotEncoder in all the data for getting the same number of features;
c) I split the data in train and test (80/20);
d) I do some normalization separately in each subset.
2) Then for the following process (feature selection,…), I use only the train set;
3) For choosing and tunning the model, I use Cross-Validation and with cross_val_score I am splitting the train set into train and validation;
4) After discovering the best model and hyperparameters I fit the whole train data into the model;
5) Finally, I evaluate the model on test data.
My questions are:
This is the correct approach?
In the evaluation step, I’m getting an 80% accuracy, however, in the final (test set) I get only 50%. The model is overfitting maybe because I have little data?
I do a test and if I change my dataset split (70/30) the accuracies are nearer to each other like 75% on evaluating a 70% on the test data. Is this normal?
Dear Jason. I have a query regarding validation dataset. Assume we are using 10-fold cross validation then why the term validation disappears for this? Why every fold cannot be considered as validation dataset?
And if we are using artificial neural network and 10-fold cross validation, then whether the training and validation fold can be used to determine the generalization of model or final test set will be used to determine the generalization as you discussed in another post regarding learning curves.
Thanks in advance
Thanks for the article Jason, but i still have a doubt in the the types of datasets.
It’s like I have read many articles on this types of datasets and most of them concluded that there should be three types of datasets like train-data set for training the model , validation-data set for hyper parameters tuning of the best model , test-data which is used for final performance measurement. But in your article you have told only two data sets are required namely, train data set and validation data set. So, my questions are:
1)which data set must be used for hyper parameters tuning using grid search cv , is it the total train data set or we need to split the train data set and use it?
2)which type of data division is better ? like dividing it into three parts (train,validation,test) or dividing it into two (train,validation) considering that we are going to do Grid Search CV too!
I got this doubt because , in one of the articles which talked about how to build your first model in that you have taken two datasets only and you called them train data set and validation data set . You didn’t talk about test data set at all , so can you please explain the reason for that
Not quite. The tutorial introduces 3 dataset: train, test and validation.
Validation is used for grid searching hyperparameters.
There is no “better”, only different approaches. You must select an approach that makes sense for your project/data – that gives you confidence that you can estimate the skill of the model when making predictions on new data.
I understood that three data sets need to be maintained , but how to use the validation data set is not clear yet . Can you please explain how to use the validation data set and train data set exactly ! like in what areas do we need to use them . For example, if we aresplitting the data set into three parts and then if we are using the train data set in cross validation using stratified cross validation for spot checking of various algorithms, then after getting the best model of all the algorithms :
1) Do we need to use grid search cv on train data and predict the values after tuning on validation data set ? or do we need to use the whole data set (train+validation) and just check the performance on the whole data set and then go for test data for final evaluation?
Can you please elaborate on the last para:
“Reference to the “test dataset” too may disappear if the cross-validation of model hyperparameters using the training dataset is nested within a broader cross-validation of the model.”
Further, I am also trying to do feature selection at the same time. Can I use the folds from CV as validation sets for determining the best features to use? Is it okay to use average feature importance from all the folds as the criterion for the same?
Thank you for the nice article! I would like to ask you some questions. Is there any difference with respect to the definition or usability of the three dataset partitions (train / validation / test) when considering different types of ML models? For example, the parametric models (neural nets) may have a different usage of the validation set during the training step compared to non-paremetric (KRR) models? Would be necessary to have a larger validation set in the case of neural nets in comparison to KRR, for instance?
Jason, thanks for the article. I like the style you explain. However, it’s still not clear for me: whether to hold out separate test set when using cross-validation. Let me refer to wikipedia:
To validate the model performance, sometimes an additional test dataset that was held out from cross-validation is used.
Thank you for the great summary and discussion of this matter !! Please advice me on this:
I have 1500 features (proteins), binary outcome, and a small dataset (235 observations):
using different ML approaches for feature selection, I ended up with different small groups of selected proteins for which I need to calculate the accuracy. For feature selection, I used the whole dataset as this disease presumed to have a complex pathology, and each observation count. Now to calculate the accuracy of the different selected groups, I’m building glm model for each group of selected features and calculate the average accuracy using 5-fold cross-validation on the whole dataset. I didn’t use a separated test set because I’m afraid that no split method would result in a test set containing observations that are representative of the complex pathology of this disease. Is it logical to think this way? or I’m getting a false accuracy by not holding out a test set from the beginning of my workflow?
After you perform CV, do you tend to use the cross validated model to predict on the test set, or you retrain the model using the full train set, applying only the best parameters?
Thanks for writing this article. If I don’t have any intuition for what my train/val/test split should be, can I try a couple different splits, tune a model for each one of them and then go with whatever split gets the best testing results or have I just defeated the point of the testing set because my model is being influenced by it?
Hi Jason, I just found your site (and a number of your articles) as I just started doing ML. I have a broad background in programming and network design so ML is my new area of study and really wanted to say you have helped clarify a lot for me so I appreciate your work, and really how you still respond to these comments years later.
I was hoping you could help me clarify just one thing as I just started learning about K-Folds.
Is validation of a model/classifier different to training one? For example, I am using Weka, selecting a classifier (NaiveBayes for example) and choosing a 10 Fold test method and it shows me the accuracy of the model. Does this mean I have a “trained” model that I can start throwing data at and begin producing results. Or, is “cross validation” only a tool that uses different methods to determine how effective a model/classifier is against a specific dataset?
After reading your articles I am thinking that validation is not training and that in simplistic terms a K-Fold simply calls the “fit()” function K times and provides a weighted accuracy score when using the the K fold as a test dataset.
I hope that makes some sense and you can clarify. Again, thank you so much for your work, you are making life just that bit easier for a lot of us.
You must first train the model in order to assess it or evaluate it.
Using k-fold cross-validation will fit k models as part of estimating how well the “algorithm” is expected to perform when used to make predictions on data not seen during the training process. All those k models are then discarded and you train one final model when you need to make predictions.
I was originally thinking the k-fold cross validation was a different way of training a model for use. However, if I am understanding correctly now and looking at your examples here (https://machinelearningmastery.com/machine-learning-in-python-step-by-step/) I think I am understanding the k-fold cv as a way to better evaluate a classifier/model to use.
For example, looking at your python example, the process is using a k-fold cv to evaluate several different models trained on our dataset. We can then choose the one we want to use based on the results (generally I assume the highest accuracy). We then choose that model and “fit” it based on our dataset (I would assume in a proper situation we would re-combine our dataset though into a single from the test/train split – I noticed you only used the training split to fit the model). From then on we have a trained model instance we can use to start running predictions on new datasets?
Again, I just wanted to say thanks for your help and articles you have put out.
validation set is also unseen data, like test sets. if so, is there any reason to fear overfitting.
or is it practically proved that using the validation set as test behave wrong?
1, is there any “leakage” with this method? i can’t find any good paper to answer this.
2, is there any “leakage” while using the validation set during training? which can have effect
while using identical test set with validation.
3, if there is, how?
4, if there is, can’t we say the general Train-Test Split is better than Walk Forward Validation
for TIME SERIES?
Correct usage of walk-forward validation does not result in leakage.
Using a “validation” set with walk-forward validation is not appropriate / not feasible. You can only use a train/test split then walk-forward on the test set.
The code part was really helpful, but I’m still confused about cases like the following:
-Two different models (ex. logistic and random forest classifier) were tuned on a validation set. The first model had 90% validation accuracy, and the second model had 85% validation accuracy.
-When the two models were evaluated on the test set, the first model had 60% test accuracy, and the second model had 85% test accuracy.
-The test and validation accuracy of the second model stayed the same, the test accuracy of the first model was much lower than its validation accuracy.
What should be done, should we start over? Would it have been better to only evaluate the first model on the test set because it had the highest validation accuracy? Is low model variance valued in this case? Which model should we choose?
Ideally we would define “how we will choose a model” before we start the project before we know any numbers.
Those are large differences. It may suggest that the harness is not stable and is not appropriate for the models/dataset. E.g. perhaps the test set is too small and not representative.
For the first case where the data is split into train, val and test, the final model is fit on train only leaving out val. Is there a reason to do it?
In k-fold cross validation example however, the final model is fit on whole train. I believe the first case could also use val for training the final model.
If we split a dataset into a training set and test set and then perform kfold cross validation on the training set in order to tune hyperparameters with each fold of validation how do we ultimately select the final model? Because each model trained using k-1 folds each time would result in a different model with potentially different features etc so how is the final ML model selected after the performing kfold CV?
Hi Jason, thank you for the tutorial, really really important for us since there is not a lot of concrete stuff out there. If I understand well, you mention …Reference to a “validation dataset” disappears if the practitioner is choosing to tune model hyperparameters using k-fold cross-validation with the training dataset… In my case I have time series data, divide the dataset to 70:30. Then, I use cross-validation (10-fold cv with the use of TimeSeriesSplit) for the 70% of the training data to tune my hyperparameters but I only evaluate the performance of my model on the 30% test data. I guess it matches with what you mention, right? Thank you very much, really appreciate your help.
If the approach you describe is appropriate for your project, then it is a good approach. It is hard for me to comment without digging into your project.
Perhaps test it and compare to other possible approaches, such as splitting the train set into train/val and using the val for tuning.
Thanks for the great article. I would like to know if it is possible to divide the k-folds (training) into two halves and use them separately to train the model (training the model with first half of the folds and then second half of the folds), while keeping the validation split unchanged? Or any article related to that would be of a great help.
Hi, I encountered again with a doubt, If you can clarify that would be great.
model = fit(fold_train, params)
skill_estimate = evaluate(model, fold_val)
So in the “Validation and Test dataset disappear” the above piece of code is mentioned.
1. Does this mean the fold_val is validation dataset?
2. Isn’t it evaluate() supposed to be used for test data?
3. Can we use this fold_val as validation_data in fit function? (as we use it general case). If not why?
If I have an imbalanced dataset, I need, for instance, to apply an undersample tecnique in every iteration of the cross Validation. Then, do I need to do so on the final evaluation? I mean, should the final part of the above pseudocode be:
# evaluate final model for comparison with other models
model = fit(undersampled_train)
skill = evaluate(model, test)
?
Hi, nested k-fold cross-validation is good practice to both select best algorithm across multiple algorithms and tuning its hyperparameters . If we stay on ‘classic’ train validation and test set practice, …I tend to use validation set both to select first the most skilled algorithm (amongst a set of algos) and tune it iteratively. When I am done with this I port my selected (and tuned) algorithm to the test set for final skill estimation. Is this still a reasonable practice?
11 : # evaluate final model for comparison with other models
12 : model = fit(train)
13 : skill = evaluate(model, test)
and your additional explanation is :
“The final model could be fit on the aggregate of the training and validation datasets.”
My question is :
Should I fit the model again using train+validation datasets after finding the final tuned hyperparameters?
11 : # evaluate final model for comparison with other models
12 : model = fit(train+validation) <<<<<<< Is this right?
13 : skill = evaluate(model, test)
I have a dataset for text classification to detect the emotion divided into three files, which are Training, development, and Testing, and Each file have one class label (not Binary) to detect one emotion such as “Sadness”, and I want to detect the emotion by Machine Learning algorithm, How can I train the model? What files do I use for training (Training or development)?
and can I do validation for one class label on the machine learning!?
Hi Jason,
I am working on CNNs on a large dataset and need to plot the train and test accuracy for each epoch. If I have already decided to use a given set of hyperparameters for the model, can I divide the entire dataset into only train and validation data and use validation accuracy as the test accuracy (as you often do in your articles for brevity)?
My main reason for doing so is the ease in plotting the validation accuracy using history callback. I am planning to manually try out several hyperparameter combinations and plot graphs for each of them.
Hi. That is a nice article but I have a question.
Can we report our results in the journal paper when we don’t use the validation set?
Or It is better to ask when we can use train and test sets without validation?
Can you give me a reference?
Thank you
Hi Jason! Thank you for all your articles. It helps me a lot but I have some questions. I just started learning machine learning last month.
My dataset has 300 rows and I have split the data to train and test in 70:30. and use kfold. since my data mainly have ‘object’ data type, I use one hot encoder.
The problem is, I got 100 accuracy for my training set (for logistic regression and gradient boosting). I assumed it is overfitting based on what I read online. But I have no idea how to solve this issue. What do you think the best solution?
By saying
“Peeking is a consequence of using test-set performance to both choose a hypothesis and evaluate it”
Is the the “choose a hypothesis” expression equivalent to “model selection” that we know or is it just “the finial trained model”? Do you mean that validation set is simply for best model selection while the test set is to add an extra layer of security by re-testing the finally selected best model?
Hi Jason,
Thanks for the great article.
I have a question: Can I include my validation set to my test set when I test the network?
I have heard from machine learning people that sometimes people add validation set to test set, because the validation is not a set that the network is trained on. Mostly, people use this idea when there is not enough data set available.
Do you have any references (articles, books) for this, that sometime people add validation to test set?
“The validation dataset is different from the test dataset that is also held back from the training of the model, but is instead used to give an unbiased estimate of the skill of the final tuned model when comparing or selecting between final models.”
But what if you have many different methods in the experimentation e.g. Random Forest (RF), SVM and Logistic Regression (LR)? Suppose a train/validation/test split is used.
All these models can be tuned independently on the validation set to get the best configuration of each. But how do you select between the three methods? I think you are saying that the best configuration of each method is evaluated on the test set, and then we choose the best among the three. Because you are using plural in “..comparing or selecting between final models”.
How come method selection is permitted on the test set but tuning is only permitted on validation? Especially if I am experimenting with many ML algorithms, I could accidentally select the one that happens to perform well on the test set.
Or am I misunderstanding your post? Is the best method selected in terms of validation or test error? The difference is between allowing only one final model to be evaluated on the test set (e.g. only SVM if it had the best validation error), or the final model of each learning method (SVM,RF,LR).
“The final model could be fit on the aggregate of the training and validation datasets”
Well, is there a reason not to add the test set in the aggregation as well, after test evaluation is completed?
Yes, best performance on holdout test set. An alternate method is to use nested cross-validation.
It is critical to evaluate on data not used to train or tune the model. Otherwise the results may be biased/misleading, causing you to potentially choose a model that looks like it performs better than it does in practice.
Hello Jason,
As always, excellent read on the train.validation and test datasets.
Even the comments conversation helped me get a lot of insights into the need for validation set.
Thank you for your prompt answer. Why? Because there is always a selection problem to reduce bias and errors with other variables? In technical terms, is there a difference between bias and errors?
Datasets are often measures of things in the world and have error in them.
The domain may have conflicting measures inherent in it.
There may be statistical noise in the measures.
Humans may have been involved and made errors
So on.
Also, all models have errors, they are not a perfect match for the underlying process that generated the data in the first place. If they were, we would not need ml – the problem would be much simpler.
my question is how properly teach CNN for MNIST to meet definitions given on this page.
1. Take MNIST Train (60000 images) and Test (10000 images)
2. Build CNN network – model
3. Learn CNN by model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=epochs,batch_size=32,verbose=2)
Given 200 epochs, is it correct select epoch with the best validation accuracy from learned 200 epochs?
of course, in this way given in the link above, the neural network is created and trained, by means of k-fold validation without MNIST test dataset only depends on Train dataset. And that’s works.
But my question is whether it is possible to train neural network given train set and test dataset, and select during learning epoch with the best validation accuracy on test set (by means of keras Callback class)? Test dataset from 10000 images from MNIST.
Hello Jason,
Thank you for all the efforts. I am new to ML. I have data, but the sample size is small, with 1500 observations. The dependant variable is 5 classes which I will treat as order from 1 to 5. I will apply cross-validation with 5 k fold for all records to see how my model will perform to predict. My question is, can I take only those prediction results (all 1500) and compare the accuracy with other models? or I have to divide the 1500 to 70/30 ratio? I thought it is enough to train and do cross-validation to measure the accuracy instead of splitting the dataset to train/test.
Great article Jason. I am still confused about something.
I trained a neural network using repeated random sub-sampling 75% train and 25% validation, and then I use all the resulting models, about 40, on my data, and average over their results to make my predictions.
Can I still call what I do a cross-validation choice?
Am I right to justify the use of the entire training set to measure performance as a valid approximation?
Thanks.
Thanks for your interesting article. In my experiment, I divide the data to 70% for 10-folds cross validation and 30% as unseen data at final stage to show how the model works on unseen and never used data. What is your suggestion?
This is an informative article. I found it very help in make the concepts of a test, validation, and test set clear to me. My only suggestion is that a clear sedition of k-fold validation be given or perhaps it’s mention removed from the article as it is not replace to the theme of the article – to clearly define the difference among test, validation and training set.
Hi Jason,
First of all thank you so much for this article.
I have a question that doesn’t want at all to be pedantic, it just to understand if I got everything right.
At line 13 of the first pseudo code, you write: model = fit(train). Would it be wrong if it were written: model = fit(train, params*) where params* are the hyperparameters chosen in the for loop, the ones with the best skill?
To give an example, if I was training a polynomial regression where K = degree of my polynomial, during the hyperparameters tuning phase I would choosing which value of K is the best for my model. Suppose that after the tuning phase I would come up with K=3 being my best choice, then the final model would be: model = fit(train, params*) where params* stands for K=3.
Is this correct?
I found this fast_ml Python package with a train_valid_test_split function:
from fast_ml.model_development import train_valid_test_split
And i want to ask you a few questions:
1 – We can use both the training and validation sets in model.fit(). Wich set should be used in model.evaluate()? The validation or the test set? If it is the validation, is there any reason to use it, since we already can use that set in model.fit()?
2 – When we are trying to predict something through model.predict(), again, wich set should be used? The validation or the test?
I have one question, I want to find the evaluation time of the model, means I want to know how many samples my model train/second. So here, should I only consider the training dataset or the validation dataset too to find the number of sample get trained/second ? One thing more, I have separate datasets for the training, validation and testing and not using the train/split function.
TIA
Regards
Wonderful article. I am frequent visitor of your site to have a better understanding of the Machine Learning concepts.
thanks for the simple but very useful read, on how to effectively use the data at hand to train and evaluate the model through the intermediate to final stage.
Hi Zhana…It is recommended that you have 3 datasets if possible…training, testing and validation. Some call the last one “dev”. It represents data that has never been seen by the trained network, much like the test dataset, however it comes from a population of data that represents what will be seen in production.
Yes, exactly. But metrics are generally reported on test set, like f1 on test set. What’s the reason some papers report f1 on test set AND f1 on dev set? Isn’t test set performance sufficient? What are they trying to show? what’s the point?
Hi Zhana…your understanding of is correct. I would recommend not following the process you may have seen some papers. There are always some looking to make their results look better than they actually are.
Given X and y:
1) Split X and y to X_train,y_train,X_test,y_test ex: 80/20
2) Keep X_test, y_test on aside and use them only for final model evaluation.
3) When using cross-validation ex. StratifiedKFold validation is performed using only the X_train and y_train.
4) After hyperparameters are tuned and the model is ready, we can evaluate the model on the testing set.
If instead of using a training set for cross-validation, we use the full set, it will cause:
a) Overfitting to the data
b) To optimistic evaluation of the performance of our algorithm as well as data leakage.
Can you please let me know if what I wrote here is correct?
What happens if using cross validation on entire data as training data set yielded an RMSE that is higher than model tested on testing data(of course assuming 80:20 split of the entire data)?
Hello, thank you for the explanation ! it helps me a lot. But for my models, after Hyperparameter tuning and choosing the best hyperparameters for my model, I would like to fit the model with the best parameters on the datasets (train + validation) to have a better learning and having a better estimation of the performances on the test set. But when I train the model with the train+validation dataset, how can I avoid overfitting during training if no more data are specified for the ‘validation data’ field in the .fit() method in python? Normally the training stops if the val loss stops decreasing to avoid overfitting (with a certain patience) but here how can I avoid overfitting of the model if all my available data (train+ validation) are used to trained the model ?
Hi,Jason.Thanks for the tutorial.
I am new to ML so I do not quite understand the meaning of ‘summarize’ in skill = summarize(skills) for k-fold validation. What exactly do we do?
Hey nice article! I’m a fan of your posts here but whenever I try to print these articles to carry with me on the metro to read there are these big floating advertisements for your machine learning tutorials that basically make it unreadable 🙁
I’m sorry to hear that. Can you send me a photo of an example?
I printed an another articles of you. And I came with the same problem as Jacob Sanders mentioned. Every page has a Ad (“Get your start in machine learning”) which cover a large space resulting in unreadability.
Hence, I can only read your articles online and cannot print it.
What a pity! : (
Sorry to hear that. They are designed to be read online.
If you want to print it, you need to convert the URL first to PDF (e.g. webpagetopdf.com). Trying to print the page directly is scattered with Jason’s opt-in. But that’s not really a problem. For that great (and free) content!
Or just sign-up and the opt-in box will disappear.
If you highlight the text you want and print using right-click the box does not appear!
Great article on Test and Validation BTW.
I offer help on printing here:
https://machinelearningmastery.com/faq/single-faq/how-can-i-print-a-tutorial-page-without-the-sign-up-form
We should split data for training,validation,testing,but what is the best way to split? It is best to make the three data set as homogeneous as possible.
It depends on the problem. This post provides some ideas, but really, you need to discover an appropriate way for your specific problem.
Yes, both the train and test sets should be representative of the problem.
why “It depends on the problem”？ I think it is best the three data set is splited as homogeneous as possible
Not always.
Sometimes old data can be less relevant if the nature of the problem changes over time.
Sometimes a small sample or even a hand crafted small sample can help an algorithm better learn the signal in mapping inputs to outputs.
And a million other reasons related to the stochastic nature of error/data/modeling.
Try PrintFriendly https://www.printfriendly.com
What do we call the set of data on which the final model is run in the field to get answers — this is not labeled data. How do we evaluate the performance of the final model in the field?
We don’t need to evaluate the performance of the final model (unless as an ongoing maintenance task).
Generally, we use train/test splits to estimate the skill of the final model. We do this robustly so that the estimate is as accurate as we can make it – to help choose between models and model configs. We use the estimate to know how good our final model is.
This post will make it clearer:
https://machinelearningmastery.com/train-final-machine-learning-model/
Hi thank you for nice article. I want to check the model to see if the model is fair and unbiased but my professor told me with cross validation or 10-fold cross validation or any of this methods we can’t confirm if the model is valid and fair. can you please give me some hints about which method I can use for this problem?
Thanks
Yes, k-fold cross validation is an excellent way to calculate an unbiased estimate of the skill of your model on unseen data.
Hi, Jason, greate article as always. I think Helen’s question is that her professor told her k-fold method can not cofirm if the model is valid. My understanding is that the test data from k-fold method actually come from the sample data with training data.
I have encountered the same problem myself. if I sample a subset as test data from original data, and the rest as training and validation data. model can perform well on three data set. But work not well on new data(not included in the original data at all). I think the way of split the test data is important. Should not use the sample method.
Thanks.
Not sure that I agree.
Studies have shown that using cross-validation with a well-set k value gives a less optimistic estimate of model performance on unseen data than a single train/test split. At least on average.
Yes, I think on average it would helped. I worked on time-series data and it performed not well. That’s why I try to figure out the reason. The following article validates my thought, this should express the problem I encountered more clearly 🙂
https://www.fast.ai/2017/11/13/validation-sets/
Time series is different. You must use walk-forward validation:
https://machinelearningmastery.com/backtest-machine-learning-models-time-series-forecasting/
I think that is because the k-fold method means your test data are sampled from the same source with the training data. so it can not completely prove the the validation of the model.
Indeed. We are only estimating the performance of the model on unseen data.
I learnt a method called rolling windows in a course I did, it represents cross validation specifically for time series data, after the data set is coerced to be indexed by time column. Then tagged/labelled a periodically (monthly, quarterly, bi-annually, annually) depending on data size, and if spanning years merged with year (YEAR+PeriodIndex = 2020Q1, 2020Q2 etc…This reps the dummy for each observation that represents which time segment that observation falls into. Similarly to k-fold cross validation techniques, the model is trained on 1- 4 Qtr periods and predicts on the 5th quarter (in my use case).
Helen, we usually process our features or variables across the entire training dataset. ie for instance when we standardize our continuous features – we take the Mu and the Sigma across the entire training set, and not on each fold of the CV. Similarly, there are times when we do some form of missing data imputation which also looks at the entire training data, and not each fold.
Meaning, when we do 5 fold or 10 fold CV, even though we explicitly train only on 4 (or 9) folds of the training data, there’s some learning which is gotten indirectly from the data that we think the model hasn’t seen
This could be a reason why your professor says this isn’t a good way to choose a final model and we will necessarily have to use hold out data
Agreed. Mu and sigma must be chosen on the training set for each fold, otherwise we get data leakage:
https://machinelearningmastery.com/data-preparation-without-data-leakage/
Nice article, really helped me to refresh my memories.
One little note: In your first code example you loop over parameters but you never use params in the loop’s body. I guess it should be used in model = fit(train, params)!?
Keep up the good work!
Glad to hear it.
Thanks for the suggestion – updated.
Hi Jason,
in the pseudocode of the part “Validation and Test Datasets Disappear”, I still didn’t understand how you used k-fold cross-validation to tune model hyperparameters with the training dataset.
Could you explain the pseudocode?
Sincerely,
Steven
Sure, each set of parameters (param) is evaluated using k-fold cross validation.
Does that help?
Hi Jason,
Great article!
Want to make sure my understanding is correct. If not, please correct me.
In general, for train-test data approach, the process is to split a given data set into 70% train data set and 30% test data set (ideally). In the training phase, we fit the model on the training data. And now to evaluate the model (i.e., to check how well the model is able to predict on unseen data), we run the model against the test data and get the predicted results. Since we already know what the expected results are, we compare/evaluate predicted and expected results to get the accuracy of the model.
If the accuracy is not up to the desired level, we repeat the above process (i.e., train the model, test, compare, train the mode, test, compare, …) until the desired accuracy is achieved.
But in this approach, we are indirectly using the test data to improve our model. So the idea of evaluating the model on unseen data is not achieved in the first place. Therefore ‘validation data set’ comes into picture and we follow the below approach.
Train the model, run the model against validation data set, compare/evaluate the output results. Repeat until a desired accuracy is achieved.
Once the desired accuracy is achieved, take the model and run it against the test data set and compare/evaluate the output results to get the accuracy.
If this accuracy meets the desired level, the model is used for production. If not, we repeat the training process but this time we obtain a new test data instead.
Correct.
It is a balancing act of not using too much influence from the “test set” to ensure we can get a final unbiased (less biased or semi-objective) estimate of model skill on unseen data.
Hi Jason,
Thank you for this article. I have a question, though. I’m new to ML and have been working on a case study on credit risk currently. My data is already divided into three different sets, each for train, validation, and test. I would start by cleaning the train data (fining NA values, removing outliers in case of a continuous dependent variable). Do I need to clean validation and test datasets before I proceed with the method given above for checking the model accuracy? Any help would be really appreciated. Thanks.
Generally, it is a good idea to perform the same data prep tasks on the other datasets.
Okay. Thanks so much.
Hi Jason,
I have a question concerning Krish’s comment, in particular this part:
“If the accuracy is not up to the desired level, we repeat the above process (i.e., train the model, test, compare, train the mode, test, compare, …) until the desired accuracy is achieved.
But in this approach, we are indirectly using the test data to improve our model. So the idea of evaluating the model on unseen data is not achieved in the first place. Therefore ‘validation data set’ comes into picture and we follow the below approach.”
I’m working on a model using sklearn in python. Given that I’m training on the train dataset with some parameters, and testing on the test dataset and then reexecuting the python file but with different model parameters for training to choose between parameters, I was wondering in what this approach is different than to iteratively train a model with different parameters, evaluate it on a validation set and test it on a test set.
Thank you in advance.
Yes, you can start to overfit the test data.
In this case, it is wise to holdback another dataset, if you can spare it, and use it to evaluate/select a final model.
Does this mean the model keeps its state between executions?
Not sure I follow.
You can save your model to file and later load it and continue training or start making predictions.
Hi Jason. Thanks for clarifying these terms. I’ll make sure i’m on the same page with colleagues in future!
Can you please clarify what you mean, in response to Krish’s comment, by “It is a balancing act of not using too much influence from the “test set””?
If you use the test dataset too much, you will over fit it.
This means you – the human operator – looking at results on the test set should be a rare thing during the project.
And does the same go for the validation set: The more iterations of train-validation we make, then the more we will be tuning parameters to the noise in the validation set (leaking data?)? Although if it is too much then presumably it will do worse on the test set anyway, ie moved from underfitting to overfitting?
Would one way to reduce the need for iteratively tuning parameters, via cycles of train-validation, be to better understand the problem, its data and the available modelling tools / algos in the first place?
Generally, yes.
Maybe. Or get more data so the effect is weak.
Thanks. If you had plenty of data, do you see any issues with using multiple validation sets?
Not at all.
To iterate on what was said/asked above by Krish (Similar question), when I learnt ml, I learnt to first take the whole data set and split it 80% (Train) / 20% (Test) (test_train_split method with 80/20 params indicated). This is a completely random (so Im assuming the index is shuffled). Then of the 80% training data, it was further split it into 5 (fold) divisions, of which 1 of 5 will rep another test set (secondary test set), and the CV procedure iterates so that every 1 of 5 subdivisions gets used as the secondary test set. Isn’t this hold out secondary test set essentially the validation set? Why does the article state that the need for a validation set disappears when K-fold CV is used? This prediction on the secondary test set of the 80% training set, is part of the evaluation of the training model, before the final prediction on unforeseen data the original 20% test set.
@Krish Awesome summary – you hit the point. Thanks for enhancing my understanding
For large datasets, you could split e.g. 95% vs. 5% (instead of 70%30%).
Reasons: you give your neural network enough data to train. 5% on a large dataset is still big for dev and/or test. Andrew Ng calls this the new way of DL splitting the data.
Great, thanks for the explanation
oh my god thanks for the summary
I wonder whether the expected result you said mean true target values of samples in test set ?
Thanks Jason!
You’re welcome.
Hi Jason,
Again a good overview. However I want to point out one problem when dividing data into these sets. If the distributions between the data sets differ in some meaningful way, the result from the test set may not be optimal. That is, one should try to have similar distributions for all sets, with similar max and min values. If not, the model may saturate or underperform for some values. I have experienced this myself, even with normalised input data. On the other hand, when dealing with multivariate data sets, this is not easy. It would be nice to read a post on this.
This is a really good point, thanks Magnus.
How would controlling the distribution per split be achieved if the the data set is shuffled?
Hi Jason,
First, Thanks for your article
But, I so confused when implement train, test, and validate split in Python.
In my implement I use Pandas and Sklearn package.
So, how to implement this in Python?
You can use the sklearn function train_test_split() to create splits of your data:
http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split
Perhaps first split your data into train/test, then split your train into train/validation sets, then use cross-validation on your training set.
That is just one approach, does that help?
Thanks for your help Jason, but
When I use cross-validation on training set. Which training set can I must use? In train/test or train/validation sets?
First split into train/test then split the train into train/validation, this last train set is used for CV.
They should have separate names, sorry about that.
I believe you are saying:
“First split into train#1/test then split the train#1 into train#2/validation, this last train#2 set is used for CV.”
However, based on the following comment in your article, I thought with CV you didn’t need a validation set:
‘Reference to a “validation dataset” disappears if the practitioner is choosing to tune model hyperparameters using k-fold cross-validation with the training dataset.’
Please clarify.
BTW: I really appreciate these well-done articles. Thank you.
Correct.
Yes, you can make it automatic by performing hyperparameter selection within cv, called nested cv.
Jason,
I have many ask to you because I still confused:
1. Do we always have to split train dataset into train/validation sets?
2. what the result if I don’t split train dataset into train/validation sets?
No you don’t have to split the data, the validation set can be useful to tune the parameters of a given model.
Hi Jason,
if I want to split data to train/test (90:10), and I want to split train again to train/validation, train/validation (90:10) right or I can split with free ratio ?
The idea of “right” really depends on your problem and your goals.
– The theory of train/test is, that you tend to overfit if you optimize on test set.
– The theory of train/dev/test is, that you optimize train/dev and only see if it works with test.
I don’t know who came up with train/test/dev. Maybe Andrew Ng? Intuitively, I like train/dev/test.
With practitioners, I don’t see it being used.
To me, Andrew Ng doesn’t count as a practitioner.
Does Jason use it? I have not seen it.
Does Francois Chollet (creator of Keras) use it? Not that I’m aware of.
It’s hard to generalize, it is better to pick the breakdown that helps you develop the best models you can for your specific project.
Hi Jason,
I just wish to appreciate you for the very nice explanation. I am clear on the terms.
But I would like you explain more to me on the tuning of model hyperparameter stuff.
Thanks Austin.
Which part of tuning do you need help with?
Hi, Dr. Jason,
Thank you for this post. It’s amazing, I have cleaned my misunderstood of the validation and test sets.
I have a doubt maybe it is out of this context but I think it is connected.
I want to plot training errors and test errors graphical to see the behavior of the two curves to determine the best parameters of a neural network, but I don’t understand where and how to extract the scores to plot.
I have already read your post: How to Implement the Backpropagation Algorithm From Scratch In Python, and learned there how to have the scores using accuracy, (I am very thankfully for that).
But now I want to plot the training and test errors in one graphic to see the behavior of the two curves, my problem is I don’t have an idea of where and how to extract this errors (I think training errors I can extract on the training process using MSE), and where and how can I extract the test errors to plot?
Please help me with this if you can. I am still developing a backpropagation algorithm from scratch and evaluate using k-fold cross-validation (that I learner your posts).
Best Regards.
You could collect all predictions/errors across CV folds or simply evaluate the model directly on a test set.
Hi, Dr. Jason,
Thank you for your reply.
I have already tried to do so, but there are two problems I find on my self:
The first problem is (my predict function returns 0 or 1 each time I call it in the loop, with this values I can calculate the error rate and the accuracy), my predict function uses the forward function that returns the outputs of output layer that are rounded to 0 or 1, so I am getting confuse If I have to calculate this errors using these outputs from the forward function inside the predict function before round to 0 or 1 (output-expected)? or I will calculate these errors inside the k-foldCV function after the prediction using the rounded values 0 or 1 (predictions – expected)?
The second problem is (In the chart of training error I plotted using a function of training errors and the epochs. But here in the test error I can’t imagine the graphic will be a function of the errors with what values since I need to plot this errors in the same graphic).
My goal is to find the best point (the needed number of epochs) to stop training the neural network by seeing the training errors beside the test errors. I am using accuracy but even I increase or decrease the number of epochs I cant see the effect in the accuracy, so I need to see these errors side by side to decide the number epochs needed to train to avoid over fitting or under fitting.
I am really stuck at this point, trying to find a way out everyday. And here is where I find most of solutions of my doubts in practice.
Best Regards
Perhaps try training multiple parallel models stopped at roughly the same time and combine their predictions in an ensemble to result in a more robust result?
Hi, Dr. Jason,
The recommended approach is new to me, and seems to be interesting, have got a post where I can see and have an idea of how it works practically?
Best regards.
Do the examples in this post help?
Let me read again the and the examples, maybe I can find out something I didn’t see in the first read.
Best regards.
Hi Jason,
What is the industry % standard to split the data into 3 data sets i.e train,validate and test ?
Yes.
Do we have the industry % for splitting the data ?
Depends on data.
Perhaps 70 for training, 30 for test. Then same again for training into training(2) and validation.
Hi;
Thanks for your beautiful explanation. I think one reason for such a confusion among many people about training, test and validation datasets is the fact that depending on different steps of data analysis we have to use the same terms for these datasets, however, these datasets will be changed and are not the same. For example, let’s say there is an analyst who wants to predict “y” from several “x”s using the Naive Bayes Classifier. S/he is going to use a training dataset and a test dataset. After prediction of “y” then s/he want to validate the model. In this case the previous test dataset may act as a validation dataset after partitioning it into training and test datasets. So, we are using validation and test terms almost equal, but depending on what is the purpose of analysis it may different based on predicting our dependent variable (using training and test datasets) or just for assessment of model performance using previous test dataset(=validation) and partitioning into training and test dataset.
Thanks for sharing.
I got to Chapter 19 in your Machine Learning Mastery with Python book and needed more explanation of a validation dataset. Google led me here and now I realize what a great complement your site info is to your books for deeper insight into some topics and/or expanded explanations. I’m really enjoying learning through your books.
Thanks Dana!
Hello Jason! It was a really good post which helped me understand the differences between the terms.
But I have a small problem. Recently I sent a manuscript to a journal and I carried out the following steps to develop the model
1) Split the Dataset into training(80%) and testing(20%)
2) Performed 5 fold CV on the training dataset to choose model hyperparameters i.e the best architecture for a DNN and then trained the model with complete training dataset
3) While training the model ie. backpropagation step I also used early stopping. I used a new dataset ( other than training and testing) to determine the maximum epochs at backprop step.
4) Finally after developing the DNN completely I tested its generalization on the test dataset which was never used before.
The reviewer said that generally ML practitioners split the data in Train, Validation and Test sets and is asking me how have I split the data?
I think my approach is good and I have written everything clearly. How do I explain that there is no need to choose a validation set when you are applying k fold CV? I dont want to blow this up since there’s only once chance to communicate with the reviewer.
A validation set can be used within each fold for tuning the model.
Thanks for replying. But during k fold cross validation we do not explicitly take a validation set. We divide the training data into k subsets and repeat the training procedure k times each time using a different subset as a validation set. Then we average out the k RMSE’s and get the optimal architecture. Once we get the optimal architecture we train on the complete training dataset. Right?
We can split each fold further into train, test and validation. It is one possible approach.
Yes, once you are happy, the model is fit on all data:
https://machinelearningmastery.com/train-final-machine-learning-model/
Okay… something like nested cross validation…but the question is whether that’s necessary. And how do I know which approach is better?
It is really problem specific, e.g. how much data and the complexity of the models.
The chosen method for estimating model skill must be convincing.
I have to compare the two strategies to choose optimal parameters of my NN. I have the following reasoning. Please tell if it is correct!
In train-validation-test split case, the validation dataset is being used to select model hyper-parameters and not to train the model hence reducing the training data might lead to higher bias. Also the validation error deoends a lot on which data points end up in the validation set and hence different validation sets might give different optimal parameters i.e. the evaluation results in higher variance.
In k fold cv , which is a more progressive procedure, each subset and hence every data point is used for validation exactly once. Since the RMSE is averaged over k subsets, the evaluation is less sensitive to the partitioning of data and variance of the resulting estimate is significantly reduced. Also since all the data points are used for training bias is also reduced.
Sorry, I’m not sure I follow.
Do you think it is incorrect?
You can choose to evaluate a model any way you wish. There are no ideas of correct. What matters is how the model actually really performs when used on new data. All these methods are an attempt to estimate this unknown quantity.
is it necessary to create a test set
You must choose how you wish to run your project and estimate the skill of models.
How to add cross validation in keras model? I don’t want to use k fold
k-fold cross-validation is cross-validation.
Hi Jason,
I have a question about image data handling.
I have thousands of road camera images that captured by the same camera for every 5 min time intervals. So my questions is that as it is being kind of time-series problem since image domain not much changing, should I need to use TimeSeriesSplit from sklearn for getting a trustable result, or do you suggest anything for me on this?
I am little confused I can’t find enough explanation when it comes to images because generally discussions are being done around numerical data splitting. Would you think to write something on images, too?
Thank you
As a series, perhaps you can use walk forward validation:
https://machinelearningmastery.com/backtest-machine-learning-models-time-series-forecasting/
Very good information Sir.
I am trying to compare two different sets of data that are millions of lines in size. One set is approximately 10% bigger than the other so in looking over the explanations presented, as well as the other links, I am not sure the K-fold perspective would be appropriate.
Is there a k fold usage that would allow for determining if the data sets are the same and being able to define why the difference would occur?
Perhaps you can use summary statistics on the datasets and compare the results using statistical tests?
Hi Sir. I have a problem. If we want to use random forest with cross validation, then the result in R show us there are accuracy. How if we want to use ROC curve for the accuracy of every fold of random forest? Thanks
I would recommend separating the CV estimate of model skill from the evaluation of the ROC curve. Consider review the curve from a single model.
Quick question: after determining the best model (fit with best hyperparms through training, with k-fold cross validation, then test predict using out-of-the-box testing data set), do we still have to do another fit() against the entire data set to finalize the model with the given hyperparms? I have always assumed that the entire set needs to be fitted one last time before release to the wild.
Correct Bernard, we do a final fit on all data right at the end once we are ready to start using the model.
More on the topic of model finalization here:
https://machinelearningmastery.com/train-final-machine-learning-model/
Note, you can pick and choose a methodology that is right for your problem. The post above is trying to present a general approach to cover many problem types.
I hope that helps.
I am still confused on how the workflow when you want to show how multiple models compare and then perform hyper-parameter tuning on the best one.
Lets say my objective is to perform supervised learning on a binary classifier by comparing 3 algorithms and then further optimizing the best one.
Workflow:
1. Separate train and test data.
2. Perform model fitting with the training data using the 3 different algorithms. Evaluate and compare the performance of each against the test data (like ROC AUC).
3. Take the best model from step 2 and perform hyper-parameter tuning with only the training data, evaluating and reporting the performance against the same test data.
My spidey sense tells me I shouldn’t be making decisions off of my test data results in step 2, but how else can I compare them? Does cross-validation really buy me anything here?
There is no best workflow, you must choose an approach that makes sense for the data you have and your project goals.
One approach would be to split the training data into train/val and tune each model to find the best config, then compare skill on the test dataset.
The train and test datasets must be representative. If not, perhaps use simple CV with a train/val split.
Hi Jason,
Thank you for the article. I am working on a problem where the training set and test set are provided separately. I am following similar approaches mentioned in yours machine learning mastery books. First, I divide the training set into train and validation sets. Then, I build the models on train set using 5-fold cross validation. I achieved the accuracy of around 99% (+/- .003%) for machine learning classifiers such as Decision Tree, Random Forest, and Extra Tree. When I apply these models on validation set, I get almost the same accuracy. Finally, I apply these models on the separately provided test set. However, the models do not give me the similar results. Is this a case of over-fitting?
Well done!
Perhaps a little overfitting if you used the validation set a few times?
Perhaps the test set is truly different to the train/validation sets, e.g. is more/less representative of the problem?
Hi Jason, thank you for your nice article. As i’m new to ML i’m still a bit confused. I am doing a binary classification on a data set about 50000 data using different ML algorithms. I manually divide the data to train and test (using 80% for training). using sklearn library, applied different classifiers without any tuning and got almost well results. Could my work be completely wrong or useless because I didn’t have a validation set and tuning? I mean is it a must?
Well done. Perhaps your problem is simple.
Test your models in a way that gives you confidence that the finding is real. That you know the model is skilful. Be skeptical.
Thank you.
And which performance metric is the best metric to be considered in the comparison of different algorithms? If i’m not wrong, the accuracy could not be a good metric to be used alone except that the data is balanced. so, for comparison should I consider all the metrics such as accuracy, precision,…,together? or for example just comparing the f1-score?
Depends on the problem and the goals of the project. There is no general answer.
I understand while building machine learning models, one uses training and validation datasets to tune the parameters and hyperparameters of the model and the testing dataset to gauge the performance of the model.
But given that in most cases, these ideal (tuned) values change with the size of the dataset, the ideal parameter and hyperparameter values are going to be different for a production training dataset. So, how does one tune these values when going to production where there is only a training dataset?
Possible solutions:
Divide the production dataset into training and validations sets. This can be done is some cases but in many cases, it is desirable to train the model on all the data available, especially in time series problems where the latest datapoints may be more valuable. Even otherwise, most models perform better with more data (except the ones that don’t). So this can’t be solution in all cases.
Train on multiple sizes of training datasets and establish the relationship between the training size and change in ideal para-hyperpara values. Then, apply this relationship while setting up the production model.
The second one seems a better solution but in itself, becomes a considerably difficult problem for which, one might use another machine learning model. Are there any existing frameworks for doing this process? (Preferable in python or tensorflow)
Are there any better solutions than these two?
It is your choice.
The intent is to use the train/validation set to find what works, confirm this via test, then fit the final model on all data using those parameters.
But the ideal parameters for the model built on all the data are going to be different from those for the train/validations sets.
This seems like a possible solution for some cases.
https://stats.stackexchange.com/a/350792
Yes, bu we want to minimize this difference with a robust estimation via our test harness.
I have a similar problem as Chris (May 24, 2018).
I took 20% off my data as a test set (stratified sampling, so the ratios of my two classes stay alike). On the 80% I conduct 10-fold CV, having 4 different classifiers to compare. This gives me 40 different models/ different sets of parameters.
I can compare these models now based on calssification metrics and can even define my “best approach”, for example taking the one with the highest average f1-score over 10 folds.
Now the following step is unclear and I just can’t find a reference in literature I could stick to (or I could quote from for my academic work): What model do I use now to get my unbiased estimate from the test set?
There are two possibilities:
1. Chose the best classifier (based on the average f1score), retrain it on the whole 80% and the use this model for the test set.
2. Chose the best classifier (based on the average f1score) and take the model in the “best” fold (highest f1score reached), use it on the test set.
Also, I also think it is interesting how the other 3 classifiers perform on the test data (either chosing the model of the “best fold” of every classifier or by retraining every classifier on the 80%), but I somehow fear that I devalue the results of the CV if I also monitor their performance on the test set. I know that under no circumstances, the test set should be used to SELECT between the models, but I think having an unbiased estimate for each model is also interesting.
Could you please comment my problem, maybe even with a literature reference? Thank you very much!
Of course, you can do whatever you like.
My suggestion would be: You find the best model config using CV (discard all the models), then re-fit a new model on all training data and evaluate it on test.
Dear Jason,
I fitted a random forest to my training dataset and prediction was very good. The predicted pattern almost exactly followed the actual value. But no so with validation data. RMS errors was 5 to 6 times compared to RMSE for training data
I tried tuning the parameters of RF and even changed the features that are inputs to model
that did not help either
what could b the reasons.
The training and validate dataset are 70/30% split of original dataset
Thanks
I have some ideas to try here:
https://machinelearningmastery.com/machine-learning-performance-improvement-cheat-sheet/
Thanks for the excellent tutorial Jason. I have a question. I am doing MNIST training using 60,000 samples and using the ‘test set’ of 10,000 samples as validation data. So in every epoch I train on 60,000 and then evaluate on 10,000. Then I report the best evaluation accuracy across all epochs. My question is – is this approach wrong?
Sounds like a good start.
Is the kfold method similar to a walk forward optimization.?..I come from a trading background with no knowledge of programing but use software package that has a wall forward optimizer.
k-fold cross-validation is for problems with no temporal ordering of observations. You can learn more here:
https://machinelearningmastery.com/k-fold-cross-validation/
Walk-forward validation is a method for evaluating models on data with a temporal ordering, learn more here:
https://machinelearningmastery.com/backtest-machine-learning-models-time-series-forecasting/
Your explanation on training data sets, validation data sets and test data sets is highly educating. In view of these explanation, how do we now differentiate between validation and testing? Can we conclude that validation takes place on the process of development of a research package, while testing takes place after the package completion to ascertain for example functionality or ability to solve an educational problem for example.
Given the goal of developing a robust estimate of the performance of your model on new data, you can choose how to structure your test harness any way you wish.
Hi,
Thanks for the article. However, suppose , I have a single data set with 50 thousand observations.(no test set is given separately), Can I divide the given dataset into 3 parts i.e train, validation and test and proceed with modelling.
Please reply.
Thanks in Advance
Sure. It comes down to whether you have sufficient data that each sub-sample is usefully representative of the broader problem that you’re modeling.
Hi, Jason, really nice Article, like always,I am big fan of your blog, I am working on EEG of Bonn university, there is, I have 11500 observations and 178 features
is it correct if I first do train/test split with rang .20, then using this training as again with range .30 train/validate ?
since I am using keras, it during validation, I can probably play with epoch and batch size only to find good model, my question is that for should I also do parameter tuning extra with this training set and validation test and with this model
and at the end, should i try the result on test set ? if the result does not work, or it was over fitting , how can i improve it
Mohammad
Find a split that makes sense for your domain and amount of data.
Training Dataset: The sample of data used to fit the model.
•Validation Dataset: The sample of data used to provide an unbiased evaluation of a model fit on the training dataset while tuning model hyperparameters. The evaluation becomes more biased as skill on the validation dataset is incorporated into the model configuration.
•Test Dataset: The sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.
appreciate the explanation Jason, and might have a vague Idea on what you have said here, but still a little foggy. so, I have quote your summary of the three definitions above.
still a bit hung up on difference between validation and test. they both are “used to provide and unbiased evaluation of a model fit” one, though (the test) is a FINAL model fit. so, let me try to say this in laymans terms. the validation set it seems to me is kind of an intermediary part that “tweaks” the training set; its intention is to improve/fine tune it. so that if we had a 60, 25, 15 split, where the 25(the validation) improved on the 60 (the training), we believe that to be better than taking the whole 85 as a training and then testing with the last 15? is that correct? thanks john
Sounds correct to me John.
This is just a heuristic though, a good practice. It is not a strict rule.
Mr Jason
Shall we do manual split of training validation and test set in different directories or through programming. In this case weights or samples get changed kindly advise the best practice
It does not matter, use the approach that you prefer.
Hey, Jason!
Thank you for the article and taking your time to answer our questions, man.
I have one question.
What can we do when, after performing cv, we get poor results (like model instability, overfitting, etc)?
I know we can get more data and redo the whole process, but what can be done when collecting more data is not possible ?
Great question, I have a list of approaches to try here:
https://machinelearningmastery.com/framework-for-better-deep-learning/
Test dataset: a general hold out dataset that you test every config. of hyper-param, after each K-fold validation, to have a standard test of the tuning ? is that right ?
You mean the split disappears to say that in the end of the model selection/tuning you do a fit with the entire dataset ?
thankyou for the good work
Yes.
Thanks.
Sir I am new to this field. Can you please guide me how validation set helps in feature selection.
You can use feature selection methods using the validation dataset, which will give independent results from the test set.
Thank you sir. But I have one question that how can I use validation set for feature selection while using k-cross validation.
Split each train set within each fold into train/validation, fit on the train and evaluation feature selection with the validation.
Hi Dr Brownlee,
I have a data set that is highly imbalanced e.g. 1,000,000 observations where only 100K obs are response=1 and I want to partition train/validate/test sets as 70/20/10.
If I balance the training set say 70K 1s & 70K 0s, do I need to
a) also balance the validation set or
b) the validation set should reflect the true proportion of the imbalanced classes?
thanks,
Lobbie
No, only the training set is balanced.
Hi Dr. Brownlee
Why the testing data are identical to training data? what is the main goal.
if the system is evaluated with the same training and testing dataset (100% for training and 100% for testing), but the obtained accuracy is less 100. what this means?
Test data is not the same as training data.
Hello Jason, have you posted an article that contains the code which uses a validation data set ? If yes could you please share the link ? and if not, could you share any other useful link which has the code that uses this validation dataset !!! It would be so helpful!!
Thanks in advance.
Almost every tutorial where I demonstrate an algorithm on a dataset uses a validation dataset.
Here’s an example:
https://machinelearningmastery.com/how-to-improve-neural-network-stability-and-modeling-performance-with-data-scaling/
Thank you very much for a very interesting read! I was hoping to hear your thoughts on the following. After using the training and validation set to choose the optimally tuned model, and after applying that optimally tuned model to the test set to get an unbiased estimate of the out-of-sample performance, would it make sense to re-estimate the model using the optimal settings using ALL the data (train + validate + test) to create the optimal model that can be applied for data that is ACTUALLY new (such as the next patient that will arrive tomorrow)? I don’t see any reason why you wouldn’t want to use the test data for training your model after you’ve obtained a good estimate of out-of-sample performance, but perhaps I am missing something. Would be really happy to hear your thoughts; thanks again!
Perhaps.
If you expect the data distribution to change/you want to monitor it, test the model daily if you can.
It comes down to trust and assumptions.
Great article. Clear distinction provided. Thank you
Thanks. I’m happy it helped.
Nice article! I have a doubt on testing data set. I have to apply the model for a new set of testing data, then how can I predict the unseen dataset. Is this future data set have been collected and analysis the procedure.? And how it can be validate the single customer using this procedure? So , Is we really need a set of new customer data..? Am I right?
If you have new data without an outcome, then you are making predictions with a final model (in production/operations), not testing the algorithm.
Does that help?
Hi Jason,
I have a question about a strategy that is working very well for me. Please let me know if this makes sense.
I split my entire data into train,validation and test sets. Then I used k-fold cross validation with Gridsearch i.e. GridsearchCV to find the best set of hyperparameters that give me the least MSE score.
Now I evaluate the performance of the above down-selected model on the validation set find out the MSE on the validation set, tweak the hyper-parameters further(Say manually) to see if I can lower the MSE on the validations set.
Then I club the train and validation sets and train the model with parameters obtained from step-2 to make predictions on my test set.
Is this an acceptable approach?
Sounds reasonable.
Hi Jason, Thank you for the post..!
I have a question if we should do this partitioning only in predictive modeling or can it be done in descriptive modeling too? I know we make models in predictive and then test accuracy where in descriptive we look for some patterns but in this too, we use k-means clustering model.
So, partitioning in descriptive is strictly NO or not recommended or can we have it?
Why do you think splitting data when developing a descriptive model would be useful?
I understand, it should ideally not be there as it is not predicting anything but had some confusion in mind, so posted this question!
No problem, when developing a descriptive model, fit it on all available data.
Hi,
the 3 sets-partition seems to be a good idea and my intuition agree with it. But if u were to answer – what is the main reason to use not only test set, but also validation set ? Why it is better to tune parameters on the other set, when test set is also held-out form training sample and we use the same test set to every method we want to compare. If we would tune parameters on test set and compare the best models of every method, why would it be worse approach ?
As i mentioned, i can fell that is is really good idea and it makes sense, but i can’t name the reason to use it in practise instead of using just test set to evaluate models and to tune parameters.
We use a validation dataset to avoid overfitting the test set – e.g. too many tests against any dataset will result in a natural hill climbing of that dataset and overfitting.
I’ve split my raw data into 70/30 training/split and the I split my training again into 70/30 training/validation. I set the number of layers and threshold and then I train my model on this last mentioned training set.
What do I do know (in R Studio) with the validation set? Do I compute (i.e. predict), or do I recreate the NN but instead of using the training data, I use the validation data?
The validation set can be used to tune the hyperparameters, e.g. parameters that result in the lowest error on the validation set.
Does that help?
Should Y_test be used in stead of Y_validation in section 5.1 Create a Validation (Test?) Dataset your post https://machinelearningmastery.com/machine-learning-in-python-step-by-step/ ?
I use the test set as val in most tutorials for brevity:
https://machinelearningmastery.com/faq/single-faq/why-do-you-use-the-test-dataset-as-the-validation-dataset
hai jason saya ada beberapa pertanyaan
1. Pada saat menerapkan model machine learning, umumnya dataset yang dimiliki dibagi menjadi dua, yaitu train data dan test data. Jelaskan mengapa harus ada pembagian dua jenis data tersebut? Dalam kasus lainnya, terkadang diperlukan juga validation data. Jelaskan apa maksud dari validation data dalam kaitannya dengan train dan test data? Bagaimana jika ada salah satu data yang tidak ada?
2. Jelaskan mengenai overfitting dan bagaimana cara yang dapat dilakukan untuk mengatasinya!
3. Jelaskan mengenai konsep dan cara kerja dari metode gradient descent (GD) untuk pelatihan model logistic regression (LR) secara lengkap! Mengapa metode GD dipastikan akan menemukan nilai bobot w yang paling baik untuk model LR?
Good questions.
One dataset is used to train the model and the other is used to evaluate the skill of the model. Validation can be used to tune the model hyperparameters prior to evaluating the model on the test set. We cannot use the train or test set for this purpose otherwise the evaluation would not be good, or valid.
More on overfitting here:
https://machinelearningmastery.com/introduction-to-regularization-to-reduce-overfitting-and-improve-generalization-error/
Gradient descent is not the best method for logistic regression, but it can be used and is a fun learning exercise. Here’s an example:
https://machinelearningmastery.com/implement-logistic-regression-stochastic-gradient-descent-scratch-python/
Hi Jason,
It’s a good article to clarify some confusions.
I’m working on cancer signature identification using qPCR data. I’ve a training/validation set and a separate test set. After the identification of the best signature (a combination of some important features) using FS and k-CV on training/validation set, I want to determine the performance of the signature on the separate test set. Since the final aim is to deliver the best signature (set of variables) to be used in diagnosis in other clinics centers, which performance evaluation method is suitable for that among:
1- method1
model = fit(train) #train set is the projection of the train data on the features containing in the selected signature
skill=evaluate(model, test)
This is the classical method, i.e modeling on train data and testing on test data
2- method2
i- taking only the separate test data and the features containing in the selected signature
ii- splitting the test data into train_test and validation_test (multiple time), and take the mean performance as follows:
model = fit(train_test) #train_test set is the projection of the train data on the features containing in the selected signature
skill=evaluate(model, validation_test)
Here, we fit the model on test data and we test it on test data using multiple resampling
Suppose both of method1 and method2 give good evaluation results. If we use method1, for reusing of our signature in other clinical centers, we must deliver the reference dataset used to find the signature and the signature (variables). However with method2, we will able to deliver only the signature (i.e the variables) to be used in other centers, and this is our objective.
Is that correct ?
Not sure I follow, sorry.
Perhaps test each approach and see which results in a more robust evaluation of your methods?
sir can you please tell me how to implement model if we have train and test datasets with dissimilar content and values then how to predict the test dataset of that new values..
example:in train dataset- {id,customerid,age,valid} valid is target
in test dataset{id,customerid,age} the values in this are different from the train dataset that is it is a new data for which we have to predict a valid column for this new data of testdataset
please reply me sir..
Ideally, each dataset should be representative of the broader problem.
This is a general requirement when modeling.
hello i have a seperate train and test data set. While cleaning the data by imputing missing values and outliers, should i clean both the train and test data.
Yes, but you can only use information from the train set to clean both sets, e.g. mean/stdev/etc. otherwise you will have a type of data leakage.
Very nice article as always, I learned a lot. I’m using a weak/distant supervision method to generate some training data. So I don’t have a training data for now, meaning no training samples with gold labels. Although, I do have a limited number of labeled samples which I want to use as my dev and test sets. I have two questions:
1) My main question is how can I make sure my test set is representative of all the cases that may happen and my model would like to learn if I’m not supposed to look at the test set since our results may become biased. How can we know test set is covering all different samples that a machine should learn and is going to be evaluated on?
2) My second question, if I randomly select a dev set and evaluate the performance of model on the dev set, is it OK if I do error analysis on dev set (e.g. look at the actual false positives and false negatives) and then go back and refine my weak supervision method? Based on your article, I realize that it may make my model in bit biased, but I’m just wondering what the best thing to do is. Since my number of labeled samples is limited, I may not be able to every time create a new sample for my test set, so at least I want to make sure I’m training my model with the help of the dev set best as I can.
Even a short response would be of great help. Thanks a lot.
Thanks!
Perhaps you can calculate statistics on both dataset and compare the sample means? It’s a good first step.
Perhaps try it and see what types of impact it has on skill and/or overfitting?
This is a great blog Jason. I appreciate your effor to put this together and patiently answer our questions. I have a basic confusion, here is my question. If it has already been answered, my apologies (I looked but could not clarify this)
So let’s say I have a dataset with 2000 samples, I do a 1000:500:500 split (training:validation: test). Say I set # of epochs to 30. At the end of each epoch, accuracy score is calculated by checking performance on validation data. Doesn’t that mean, the model is looking at the same validation data set multiple times?? Then what’s the point of validation holdout if the data is visited multiple times like training data?
Thanks!
Yes, the validation set will be evaluated at the end of each epoch – but it is not used to train the model, only to evaluate it objectively (somewhat).
Does that help?
Yes, it kind of does. But still, if the model is going over the validation data to calculate the evaluation score, does it impact reliability? (I understand that the parameters are not updated during evaluation) but the validation data is not ‘unseen’ after the first epoch. Thanks
No.
Unless you use the scores a lot and you – yourself – the human operator – begin to overfit the validation set with the choice of experiments to run. This can happen.
Hi Jason
Is it right to split data in traing validation and test sets in 60%20%20%?fit the model than validate the model on validation set by getting best hyperparameters /hypertunning and stopped traing to best epoch to avoid obeefotirng and than applied the tuned model on test data for prediction?
It is one approach, there are no “best” approaches.
See what works well for your specific dataset.
Hi. Can I see final hypertunning paprameters? And I use simple validation for hypertunning than stopped training to 30 epochs when validation loss is less.
2nd I want to ask model.predict does the prediction work? as I saw in your examples too. 3rd binary cross entropy can be used for regression too or just for classification problem in CNN for time series data? I am confused about this. If you have link kindly share it. Thanks
You can print or save hyperparameters.
More on model.predict() for keras here:
https://machinelearningmastery.com/how-to-make-classification-and-regression-predictions-for-deep-learning-models-in-keras/
Cross entropy is for classification only.
For time series data prediction like wind power prediction should I use mse as loss function or cross entropy in CNN?
If you are predicting a numerical value, use mse, if you are predicting a class label, use cross entropy.
https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/
I am using CNN for time series data of wind power. Comparing actual VS predicted and calculating mse as performance metric. But as you said before you can’t use cross entropy for regression problem. I am predicting power. So it’s ok to use cross entropy for predicting power definitely in numerical form or should use mean squared error as loss function in CNN.
Sounds like you should be using mean squared error loss.
I am using binary cross entropy as loss function for time series prediction of wind power and performance is based on mse. Not mean squared error and using pytorch in python.
I think that is odd unless the target is numerical.
i used sigmoid in fully connected layer not softmax and got good results. is it ok
Sigmoid in the output layer is for binary classification tasks or multi-label tasks, but not multi-class classification.
Thank you very much Jason for this article, it’s a lifesaver.
For me it’s always better to deal with numbers, let’s say we have a 1000 data samples, from which 66% will be splitted into training and 33% for testing the final model, and am using a 10 cross validations, now my problem arises with the validation and the cross validation percentages.
What I understood is that we’ll build 10 models from the training data, each model uses (10%) from the training data (which is 0.1*66% of the total data set), and validate it using different 10% training data, from those 10 models we tune the final model’s parameters, and use the 33% testing data to get a final estimation of the model.
And in both Validation and Training, we got an unbiased estimation, did I get it right?
Yes, if you have enough data, this division can be sensible.
Hi Jason, in this article you speack of independant testing set. What does it mean ? Is it datas take from the same data set as the train data but that are different. Or does it mean that this independant testing set is made with data that are completly different from the training set ?
It means test set is not used to train the model.
Ok thank you for your reply. I asked you this question because I work on a chatbot and my team wants to use the data revored by the chatbot only for the testing set and not for the training set (and generate the training data ourself). Does it make any sense ? Because from my point of view we should put the recovered data in the training and testing set.
It really depends on the goals of the test harness – what is being tested/evalauted.
We want to evaluate the chatbot recognitation of the sentence’s meaning.
I don’t have tutorials on chatbots, perhaps check the literature on the topic?
nice article, i have just one doubt, i heard that we must use the test dataset to evalute the performance of two or more different models. But if we use the exactly same data for train and validate both models we could not eliminate the use of a test dataset ? Because both are using the same data, so they have the same chance to fit in the validation dataset, theoretically.
Yes, if you have enough data, you should have separate train, test and validation sets.
Dear Jason,
Thanks for the great post.
I am a bit puzzled by the nested CVs you mention towards the end (I have a small dataset so it is quite relevant to me not to leave out chuncks of data if possible):
1. assume you do 10-fold CV on the whole dataset
2. at each iteration you put away a fold for testing and you do CV on the remaining 9 to tune the model hyperparameters
However, for every iteration, in step 2 you will probably choose a different set of hyperparameters. Which then do you use for the final model? A natural answer is to choose the set with lowest test error, computed through CV on the whole data set. But then, why not to directly choose your hyperparameters through CV on the whole data set? (The latter is slightly unsatisfactory as it would mean validation \approx test, but I cannot see a way out, especially because it is likely that no two sets of hyperparameters are equal in step 2 above, so no majority voting either.)
Many thanks,
JC
Correct.
Correct again, a different set each time.
You are in effect cross-validating the model configuration process. The result will indicate that: when you use “this” model with “this” tuning process the results are like “that” on average.
Nested CV might be the most robust approach at the moment for small datasets. I will write a dedicated post on the topic ASAP.
Hi, first of all, thanks, these posts are really helpful.
So I have a small dataset (around 1000 rows) for classification, and my doubt is about the process that I am following:
1) In the first place, I prepare the data.
a) I remove the columns that might be useless;
b) I use oneHotEncoder in all the data for getting the same number of features;
c) I split the data in train and test (80/20);
d) I do some normalization separately in each subset.
2) Then for the following process (feature selection,…), I use only the train set;
3) For choosing and tunning the model, I use Cross-Validation and with cross_val_score I am splitting the train set into train and validation;
4) After discovering the best model and hyperparameters I fit the whole train data into the model;
5) Finally, I evaluate the model on test data.
My questions are:
This is the correct approach?
In the evaluation step, I’m getting an 80% accuracy, however, in the final (test set) I get only 50%. The model is overfitting maybe because I have little data?
I do a test and if I change my dataset split (70/30) the accuracies are nearer to each other like 75% on evaluating a 70% on the test data. Is this normal?
Perhaps. There is no such thing as “correct” or “normal”, there are just different approaches.
Use a test harness that you trust to give a reliable estimate of model performance.
This process might also give you ideas:
https://machinelearningmastery.com/start-here/#process
I like nested repeated stratified k-fold cross validation for classification these days.
Nice Article.
Dear Jason. I have a query regarding validation dataset. Assume we are using 10-fold cross validation then why the term validation disappears for this? Why every fold cannot be considered as validation dataset?
And if we are using artificial neural network and 10-fold cross validation, then whether the training and validation fold can be used to determine the generalization of model or final test set will be used to determine the generalization as you discussed in another post regarding learning curves.
Thanks in advance
It is a test set, not a val set. A validation set would be split from the train component of the cv process – such as if we did grid search within CV.
Hey Jason! As usual, lovely article! I was working on a project and noticed something rather peculiar :
“The mean of my cross validation (k=10) accuracies were consistently lower than the accuracy on my final test data”.
Could you help me in understanding why this happens?
Thanks.
Perhaps your test data is too small or not representative of the dataset?
Hey Jason! Great article.
I don’t know if you have already asked that question.
Does it make sense to train/test split (80%-20%) then use k-fold cross-validation on X_train and y_train?
I say this because in the vast majority I see k-fold cross-validation being used in the entire dataset
Yes, it can, the al set can be used as a final check of skill as long as it is representative of the broader problem.
Great . i got the definitions .
Well done!
Thanks for the article Jason, but i still have a doubt in the the types of datasets.
It’s like I have read many articles on this types of datasets and most of them concluded that there should be three types of datasets like train-data set for training the model , validation-data set for hyper parameters tuning of the best model , test-data which is used for final performance measurement. But in your article you have told only two data sets are required namely, train data set and validation data set. So, my questions are:
1)which data set must be used for hyper parameters tuning using grid search cv , is it the total train data set or we need to split the train data set and use it?
2)which type of data division is better ? like dividing it into three parts (train,validation,test) or dividing it into two (train,validation) considering that we are going to do Grid Search CV too!
I got this doubt because , in one of the articles which talked about how to build your first model in that you have taken two datasets only and you called them train data set and validation data set . You didn’t talk about test data set at all , so can you please explain the reason for that
Can you please help me out ?
Not quite. The tutorial introduces 3 dataset: train, test and validation.
Validation is used for grid searching hyperparameters.
There is no “better”, only different approaches. You must select an approach that makes sense for your project/data – that gives you confidence that you can estimate the skill of the model when making predictions on new data.
Yes, most of my tutorials keep things simple:
https://machinelearningmastery.com/faq/single-faq/why-do-you-use-the-test-dataset-as-the-validation-dataset
I understood that three data sets need to be maintained , but how to use the validation data set is not clear yet . Can you please explain how to use the validation data set and train data set exactly ! like in what areas do we need to use them . For example, if we aresplitting the data set into three parts and then if we are using the train data set in cross validation using stratified cross validation for spot checking of various algorithms, then after getting the best model of all the algorithms :
1) Do we need to use grid search cv on train data and predict the values after tuning on validation data set ? or do we need to use the whole data set (train+validation) and just check the performance on the whole data set and then go for test data for final evaluation?
Validation dataset is typically used to tune the model’s performance.
One approach is to do a grid search within each cross-validation fold, this is called nested cross-validation.
Thank you for the article, I have a question too what if you have the testing and the training dataset and you want the validation dataset?
I don’t follow, what do you mean? Can you elaborate?
Thanks for the exhaustive article, Jason.
Can you please elaborate on the last para:
“Reference to the “test dataset” too may disappear if the cross-validation of model hyperparameters using the training dataset is nested within a broader cross-validation of the model.”
Further, I am also trying to do feature selection at the same time. Can I use the folds from CV as validation sets for determining the best features to use? Is it okay to use average feature importance from all the folds as the criterion for the same?
You’re welcome.
I am suggesting that no broader split into train/test/val is needed when you use nested cross-validation.
Feature selection can be one step in the modeling pipeline that you are cross-validating.
Hi Jason,
Thank you for the nice article! I would like to ask you some questions. Is there any difference with respect to the definition or usability of the three dataset partitions (train / validation / test) when considering different types of ML models? For example, the parametric models (neural nets) may have a different usage of the validation set during the training step compared to non-paremetric (KRR) models? Would be necessary to have a larger validation set in the case of neural nets in comparison to KRR, for instance?
Not really. The data should be representative (iid).
Jason, thanks for the article. I like the style you explain. However, it’s still not clear for me: whether to hold out separate test set when using cross-validation. Let me refer to wikipedia:
To validate the model performance, sometimes an additional test dataset that was held out from cross-validation is used.
(https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets)
What do you think?
Typically you would not.
Dear Jason,
Thank you for the great summary and discussion of this matter !! Please advice me on this:
I have 1500 features (proteins), binary outcome, and a small dataset (235 observations):
using different ML approaches for feature selection, I ended up with different small groups of selected proteins for which I need to calculate the accuracy. For feature selection, I used the whole dataset as this disease presumed to have a complex pathology, and each observation count. Now to calculate the accuracy of the different selected groups, I’m building glm model for each group of selected features and calculate the average accuracy using 5-fold cross-validation on the whole dataset. I didn’t use a separated test set because I’m afraid that no split method would result in a test set containing observations that are representative of the complex pathology of this disease. Is it logical to think this way? or I’m getting a false accuracy by not holding out a test set from the beginning of my workflow?
Many thanks in advance for your input
I think you’re asking how to calculate accuracy for different classes.
We cannot as accuracy is a global score, instead you can use precision or recall for each class, this will help:
https://machinelearningmastery.com/framework-for-imbalanced-classification-projects/
Also, when evaluating a multi-class model, I recommend using stratified k-fold cross-validation so that each fold has the same balance of examples across the classes as the original dataset:
https://machinelearningmastery.com/cross-validation-for-imbalanced-classification/
After you perform CV, do you tend to use the cross validated model to predict on the test set, or you retrain the model using the full train set, applying only the best parameters?
No, models from cross-validation are discarded.
Retrain on all data with full dataset:
https://machinelearningmastery.com/train-final-machine-learning-model/
Hey Jason,
Thanks for writing this article. If I don’t have any intuition for what my train/val/test split should be, can I try a couple different splits, tune a model for each one of them and then go with whatever split gets the best testing results or have I just defeated the point of the testing set because my model is being influenced by it?
You’re welcome.
Yes, or explore how sensitive models are to dataset size and use that to inform split sizes.
Hi Jason, I just found your site (and a number of your articles) as I just started doing ML. I have a broad background in programming and network design so ML is my new area of study and really wanted to say you have helped clarify a lot for me so I appreciate your work, and really how you still respond to these comments years later.
I was hoping you could help me clarify just one thing as I just started learning about K-Folds.
Is validation of a model/classifier different to training one? For example, I am using Weka, selecting a classifier (NaiveBayes for example) and choosing a 10 Fold test method and it shows me the accuracy of the model. Does this mean I have a “trained” model that I can start throwing data at and begin producing results. Or, is “cross validation” only a tool that uses different methods to determine how effective a model/classifier is against a specific dataset?
After reading your articles I am thinking that validation is not training and that in simplistic terms a K-Fold simply calls the “fit()” function K times and provides a weighted accuracy score when using the the K fold as a test dataset.
I hope that makes some sense and you can clarify. Again, thank you so much for your work, you are making life just that bit easier for a lot of us.
Thanks!
You must first train the model in order to assess it or evaluate it.
Using k-fold cross-validation will fit k models as part of estimating how well the “algorithm” is expected to perform when used to make predictions on data not seen during the training process. All those k models are then discarded and you train one final model when you need to make predictions.
Does that help?
It does, I hope haha.
I was originally thinking the k-fold cross validation was a different way of training a model for use. However, if I am understanding correctly now and looking at your examples here (https://machinelearningmastery.com/machine-learning-in-python-step-by-step/) I think I am understanding the k-fold cv as a way to better evaluate a classifier/model to use.
For example, looking at your python example, the process is using a k-fold cv to evaluate several different models trained on our dataset. We can then choose the one we want to use based on the results (generally I assume the highest accuracy). We then choose that model and “fit” it based on our dataset (I would assume in a proper situation we would re-combine our dataset though into a single from the test/train split – I noticed you only used the training split to fit the model). From then on we have a trained model instance we can use to start running predictions on new datasets?
Again, I just wanted to say thanks for your help and articles you have put out.
Correct. I call it the final model:
https://machinelearningmastery.com/train-final-machine-learning-model/
thank you very much!
what is the problem if we use similar validation and test set?
Similar is good, identical is bad.
It may lead to optimistic evaluation of model performance via overfitting.
validation set is also unseen data, like test sets. if so, is there any reason to fear overfitting.
or is it practically proved that using the validation set as test behave wrong?
Yes, if the test or validation set is too small or not representative or the validation set is not used to stop training at the point of overfitting.
thank you for your response and your generous tutorials!
but i am continuing to ask, i believe you can make me understand this.
the main problem i face, which makes me continue to ask is the concept called “leaking”.
i have read your tutorial on Walk Forward Validation
(https://machinelearningmastery.com/backtest-machine-learning-models-time-series-forecasting/)
other tutorials call this technique also time series splits, and states its disadvantage as:
“Such overlapping samples are unlikely to be independent, leading to information leaking from the train set into the validation set.”
(https://medium.com/@samuel.monnier/cross-validation-tools-for-time-series-ffa1a5a09bf9)
“However, this may introduce leakage from future data to the model. The model will observe future patterns to forecast and try to memorize them.”
(https://hub.packtpub.com/cross-validation-strategies-for-time-series-forecasting-tutorial/)
therefore, the main QUESTIONS i raise are:
1, is there any “leakage” with this method? i can’t find any good paper to answer this.
2, is there any “leakage” while using the validation set during training? which can have effect
while using identical test set with validation.
3, if there is, how?
4, if there is, can’t we say the general Train-Test Split is better than Walk Forward Validation
for TIME SERIES?
Correct usage of walk-forward validation does not result in leakage.
Using a “validation” set with walk-forward validation is not appropriate / not feasible. You can only use a train/test split then walk-forward on the test set.
The code part was really helpful, but I’m still confused about cases like the following:
-Two different models (ex. logistic and random forest classifier) were tuned on a validation set. The first model had 90% validation accuracy, and the second model had 85% validation accuracy.
-When the two models were evaluated on the test set, the first model had 60% test accuracy, and the second model had 85% test accuracy.
-The test and validation accuracy of the second model stayed the same, the test accuracy of the first model was much lower than its validation accuracy.
What should be done, should we start over? Would it have been better to only evaluate the first model on the test set because it had the highest validation accuracy? Is low model variance valued in this case? Which model should we choose?
Ideally we would define “how we will choose a model” before we start the project before we know any numbers.
Those are large differences. It may suggest that the harness is not stable and is not appropriate for the models/dataset. E.g. perhaps the test set is too small and not representative.
I think you’re right about the test set, thank you for your reply!
You’re welcome.
For the first case where the data is split into train, val and test, the final model is fit on train only leaving out val. Is there a reason to do it?
In k-fold cross validation example however, the final model is fit on whole train. I believe the first case could also use val for training the final model.
Thank you
rajesh
Yes, typically val is used to tune hyperparameters or in early stopping.
Hello,
If we split a dataset into a training set and test set and then perform kfold cross validation on the training set in order to tune hyperparameters with each fold of validation how do we ultimately select the final model? Because each model trained using k-1 folds each time would result in a different model with potentially different features etc so how is the final ML model selected after the performing kfold CV?
Thank you
Yes, you can use nested cross validation which will create the validation set automatically for you.
See this as an example:
https://machinelearningmastery.com/nested-cross-validation-for-machine-learning-with-python/
Hi Jason, thank you for the tutorial, really really important for us since there is not a lot of concrete stuff out there. If I understand well, you mention …Reference to a “validation dataset” disappears if the practitioner is choosing to tune model hyperparameters using k-fold cross-validation with the training dataset… In my case I have time series data, divide the dataset to 70:30. Then, I use cross-validation (10-fold cv with the use of TimeSeriesSplit) for the 70% of the training data to tune my hyperparameters but I only evaluate the performance of my model on the 30% test data. I guess it matches with what you mention, right? Thank you very much, really appreciate your help.
You’re welcome.
Hi Jason, I have also included a question in my comment above. Could you please give me feedback? Thank you very very much 🙂
If the approach you describe is appropriate for your project, then it is a good approach. It is hard for me to comment without digging into your project.
Perhaps test it and compare to other possible approaches, such as splitting the train set into train/val and using the val for tuning.
Awesome! Thank you.
You’re welcome.
Thanks for the great article. I would like to know if it is possible to divide the k-folds (training) into two halves and use them separately to train the model (training the model with first half of the folds and then second half of the folds), while keeping the validation split unchanged? Or any article related to that would be of a great help.
Sure, you can design the test harness any way you like – as long as you trust the results.
Great! Thank you.
You’re welcome.
Hi, I encountered again with a doubt, If you can clarify that would be great.
model = fit(fold_train, params)
skill_estimate = evaluate(model, fold_val)
So in the “Validation and Test dataset disappear” the above piece of code is mentioned.
1. Does this mean the fold_val is validation dataset?
2. Isn’t it evaluate() supposed to be used for test data?
3. Can we use this fold_val as validation_data in fit function? (as we use it general case). If not why?
I recommend this tutorial:
https://machinelearningmastery.com/k-fold-cross-validation/
Thank you
You’re welcome.
Hi,
If I have an imbalanced dataset, I need, for instance, to apply an undersample tecnique in every iteration of the cross Validation. Then, do I need to do so on the final evaluation? I mean, should the final part of the above pseudocode be:
# evaluate final model for comparison with other models
model = fit(undersampled_train)
skill = evaluate(model, test)
?
Thanks in advance!
Yes, the procedure would be applied in each step of a CV.
Yes, once you choose a procedure, it would be applied to the entire dataset in order to prepare a final model for making predictions on new data.
Hi, nested k-fold cross-validation is good practice to both select best algorithm across multiple algorithms and tuning its hyperparameters . If we stay on ‘classic’ train validation and test set practice, …I tend to use validation set both to select first the most skilled algorithm (amongst a set of algos) and tune it iteratively. When I am done with this I port my selected (and tuned) algorithm to the test set for final skill estimation. Is this still a reasonable practice?
There is no “best” way, just lots of different ways.
If your process works well for you, use it.
Hi ~~~
Thanks for the amazing article.
In your article, code line number 11~13 :
11 : # evaluate final model for comparison with other models
12 : model = fit(train)
13 : skill = evaluate(model, test)
and your additional explanation is :
“The final model could be fit on the aggregate of the training and validation datasets.”
My question is :
Should I fit the model again using train+validation datasets after finding the final tuned hyperparameters?
11 : # evaluate final model for comparison with other models
12 : model = fit(train+validation) <<<<<<< Is this right?
13 : skill = evaluate(model, test)
Yes, typically the final model is fit on all data and used to start making predictions on new data.
Thank you for your response and I have studied using your helpful book ~!
You’re welcome.
Hi Jason, Thank you for your great tutorial!
I have a dataset for text classification to detect the emotion divided into three files, which are Training, development, and Testing, and Each file have one class label (not Binary) to detect one emotion such as “Sadness”, and I want to detect the emotion by Machine Learning algorithm, How can I train the model? What files do I use for training (Training or development)?
and can I do validation for one class label on the machine learning!?
You’re welcome.
Perhaps start by loading the data into memory and organizing it into samples with input and output elements.
Hi Jason,
I am working on CNNs on a large dataset and need to plot the train and test accuracy for each epoch. If I have already decided to use a given set of hyperparameters for the model, can I divide the entire dataset into only train and validation data and use validation accuracy as the test accuracy (as you often do in your articles for brevity)?
My main reason for doing so is the ease in plotting the validation accuracy using history callback. I am planning to manually try out several hyperparameter combinations and plot graphs for each of them.
Thanks in advance!
This tutorial will help:
https://machinelearningmastery.com/display-deep-learning-model-training-history-in-keras/
Hi. That is a nice article but I have a question.
Can we report our results in the journal paper when we don’t use the validation set?
Or It is better to ask when we can use train and test sets without validation?
Can you give me a reference?
Thank you
Thanks.
You can use k-fold cross-validation and report the mean and standard deviation of model performance:
https://machinelearningmastery.com/k-fold-cross-validation/
Hi Jason! Thank you for all your articles. It helps me a lot but I have some questions. I just started learning machine learning last month.
My dataset has 300 rows and I have split the data to train and test in 70:30. and use kfold. since my data mainly have ‘object’ data type, I use one hot encoder.
The problem is, I got 100 accuracy for my training set (for logistic regression and gradient boosting). I assumed it is overfitting based on what I read online. But I have no idea how to solve this issue. What do you think the best solution?
This is a common question that I answer here:
https://machinelearningmastery.com/faq/single-faq/what-does-it-mean-if-i-have-0-error-or-100-accuracy
By saying
“Peeking is a consequence of using test-set performance to both choose a hypothesis and evaluate it”
Is the the “choose a hypothesis” expression equivalent to “model selection” that we know or is it just “the finial trained model”? Do you mean that validation set is simply for best model selection while the test set is to add an extra layer of security by re-testing the finally selected best model?
Validation can be used to tune the best model identified by the test set.
Hi Jason,
Thanks for the great article.
I have a question: Can I include my validation set to my test set when I test the network?
I have heard from machine learning people that sometimes people add validation set to test set, because the validation is not a set that the network is trained on. Mostly, people use this idea when there is not enough data set available.
Do you have any references (articles, books) for this, that sometime people add validation to test set?
You’re welcome.
No. It is probably a bad idea to include val in test. They should be kept separate.
Hi, Jason!
Just wanted to say how much I love your website. You give the clearer ML explanations that I’ve ever read.
Thank you for your kind words and support!
“The validation dataset is different from the test dataset that is also held back from the training of the model, but is instead used to give an unbiased estimate of the skill of the final tuned model when comparing or selecting between final models.”
But what if you have many different methods in the experimentation e.g. Random Forest (RF), SVM and Logistic Regression (LR)? Suppose a train/validation/test split is used.
All these models can be tuned independently on the validation set to get the best configuration of each. But how do you select between the three methods? I think you are saying that the best configuration of each method is evaluated on the test set, and then we choose the best among the three. Because you are using plural in “..comparing or selecting between final models”.
How come method selection is permitted on the test set but tuning is only permitted on validation? Especially if I am experimenting with many ML algorithms, I could accidentally select the one that happens to perform well on the test set.
Or am I misunderstanding your post? Is the best method selected in terms of validation or test error? The difference is between allowing only one final model to be evaluated on the test set (e.g. only SVM if it had the best validation error), or the final model of each learning method (SVM,RF,LR).
“The final model could be fit on the aggregate of the training and validation datasets”
Well, is there a reason not to add the test set in the aggregation as well, after test evaluation is completed?
Yes, best performance on holdout test set. An alternate method is to use nested cross-validation.
It is critical to evaluate on data not used to train or tune the model. Otherwise the results may be biased/misleading, causing you to potentially choose a model that looks like it performs better than it does in practice.
Hello Jason,
As always, excellent read on the train.validation and test datasets.
Even the comments conversation helped me get a lot of insights into the need for validation set.
Keep sharing your good work !!
Thanks a ton.
Thanks, I’m happy to hear that!
Dear Jason,
Thank your for this clear article. Is the training, validation and testing data sets can be always free of errors?
Best,
C
You’re welcome.
No, datasets almost always contain error.
Thank you for your prompt answer. Why? Because there is always a selection problem to reduce bias and errors with other variables? In technical terms, is there a difference between bias and errors?
Bias is a type of error.
Datasets are often measures of things in the world and have error in them.
The domain may have conflicting measures inherent in it.
There may be statistical noise in the measures.
Humans may have been involved and made errors
So on.
Also, all models have errors, they are not a perfect match for the underlying process that generated the data in the first place. If they were, we would not need ml – the problem would be much simpler.
Thank! Very helpful!
You’re welcome!
Hello,
my question is how properly teach CNN for MNIST to meet definitions given on this page.
1. Take MNIST Train (60000 images) and Test (10000 images)
2. Build CNN network – model
3. Learn CNN by model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=epochs,batch_size=32,verbose=2)
Given 200 epochs, is it correct select epoch with the best validation accuracy from learned 200 epochs?
Perhaps this will help:
https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-from-scratch-for-mnist-handwritten-digit-classification/
May I ask for contact at [email protected]?
I am preparing new algorithm for CNN classification and answer on this question is very important!
JK
You can contact me any time directly here:
https://machinelearningmastery.com/contact/
of course, in this way given in the link above, the neural network is created and trained, by means of k-fold validation without MNIST test dataset only depends on Train dataset. And that’s works.
But my question is whether it is possible to train neural network given train set and test dataset, and select during learning epoch with the best validation accuracy on test set (by means of keras Callback class)? Test dataset from 10000 images from MNIST.
(X_test,y_test),epochs=epochs,batch_size=32,verbose=2, callback)
Record best test accuracy, and save as model for predictions?
I believe you might be referring to early stopping:
https://machinelearningmastery.com/how-to-stop-training-deep-neural-networks-at-the-right-time-using-early-stopping/
Just started with ML. This post really helped me to grasp the difference. Thanks a lot!
You’re welcome!
I’m always getting help with your data, thank you.
Do you have any additional articles, journals, or materials to recommend on the importance of validation?
I want to more!!
XD
thank u.
Not off hand.
OK
🙂
Hello Jason,
Thank you for all the efforts. I am new to ML. I have data, but the sample size is small, with 1500 observations. The dependant variable is 5 classes which I will treat as order from 1 to 5. I will apply cross-validation with 5 k fold for all records to see how my model will perform to predict. My question is, can I take only those prediction results (all 1500) and compare the accuracy with other models? or I have to divide the 1500 to 70/30 ratio? I thought it is enough to train and do cross-validation to measure the accuracy instead of splitting the dataset to train/test.
Regards!
You would compare the mean from k-fold cross-validation results to the mean of other k-fold cross-validation results.
Great article Jason. I am still confused about something.
I trained a neural network using repeated random sub-sampling 75% train and 25% validation, and then I use all the resulting models, about 40, on my data, and average over their results to make my predictions.
Can I still call what I do a cross-validation choice?
Am I right to justify the use of the entire training set to measure performance as a valid approximation?
Thanks.
No, it is a repeated train/test split. This approach often has a more biased estimate (optimistic) of performance than a repeated k-fold CV.
Hi Jason,
Thanks for your interesting article. In my experiment, I divide the data to 70% for 10-folds cross validation and 30% as unseen data at final stage to show how the model works on unseen and never used data. What is your suggestion?
If you get a billion samples, even holding out 1% of data for final stage is 10 million samples. Therefore, it all depends your particular situation.
This is an informative article. I found it very help in make the concepts of a test, validation, and test set clear to me. My only suggestion is that a clear sedition of k-fold validation be given or perhaps it’s mention removed from the article as it is not replace to the theme of the article – to clearly define the difference among test, validation and training set.
Thanks for the suggestion. You’ll probably also like this post as well: https://machinelearningmastery.com/training-validation-test-split-and-cross-validation-done-right/
Hi Jason,
First of all thank you so much for this article.
I have a question that doesn’t want at all to be pedantic, it just to understand if I got everything right.
At line 13 of the first pseudo code, you write: model = fit(train). Would it be wrong if it were written: model = fit(train, params*) where params* are the hyperparameters chosen in the for loop, the ones with the best skill?
To give an example, if I was training a polynomial regression where K = degree of my polynomial, during the hyperparameters tuning phase I would choosing which value of K is the best for my model. Suppose that after the tuning phase I would come up with K=3 being my best choice, then the final model would be: model = fit(train, params*) where params* stands for K=3.
Is this correct?
Yes, you’re correct. That’s pseudo code only, hence it is a bit imprecise. You should see a working example here: https://machinelearningmastery.com/training-validation-test-split-and-cross-validation-done-right/
Very nice post!
I found this fast_ml Python package with a train_valid_test_split function:
from fast_ml.model_development import train_valid_test_split
And i want to ask you a few questions:
1 – We can use both the training and validation sets in model.fit(). Wich set should be used in model.evaluate()? The validation or the test set? If it is the validation, is there any reason to use it, since we already can use that set in model.fit()?
2 – When we are trying to predict something through model.predict(), again, wich set should be used? The validation or the test?
(1) use test set for evaluate()
(2) if you call predict() you use some new data (which assume you deployed your model in real world), or use test set
Hi, Adrian
Again a great post
I have one question, I want to find the evaluation time of the model, means I want to know how many samples my model train/second. So here, should I only consider the training dataset or the validation dataset too to find the number of sample get trained/second ? One thing more, I have separate datasets for the training, validation and testing and not using the train/split function.
TIA
Regards
Hi HS…This article on bench marking may be of interest to you:
https://www.neuraldesigner.com/blog/how-to-benchmark-the-performance-of-machine-learning-platforms
Hello Jason,
Wonderful article. I am frequent visitor of your site to have a better understanding of the Machine Learning concepts.
thanks for the simple but very useful read, on how to effectively use the data at hand to train and evaluate the model through the intermediate to final stage.
Thanks so much for sharing.
Warm regards,
Poonam
Some papers report performance on dev set in addition to test set. what’s the point in doing this? does comparing them have benefit?
Hi Zhana…It is recommended that you have 3 datasets if possible…training, testing and validation. Some call the last one “dev”. It represents data that has never been seen by the trained network, much like the test dataset, however it comes from a population of data that represents what will be seen in production.
Yes, exactly. But metrics are generally reported on test set, like f1 on test set. What’s the reason some papers report f1 on test set AND f1 on dev set? Isn’t test set performance sufficient? What are they trying to show? what’s the point?
Hi Zhana…your understanding of is correct. I would recommend not following the process you may have seen some papers. There are always some looking to make their results look better than they actually are.
I just want to make sure I understood correctly.
Given X and y:
1) Split X and y to X_train,y_train,X_test,y_test ex: 80/20
2) Keep X_test, y_test on aside and use them only for final model evaluation.
3) When using cross-validation ex. StratifiedKFold validation is performed using only the X_train and y_train.
4) After hyperparameters are tuned and the model is ready, we can evaluate the model on the testing set.
If instead of using a training set for cross-validation, we use the full set, it will cause:
a) Overfitting to the data
b) To optimistic evaluation of the performance of our algorithm as well as data leakage.
Can you please let me know if what I wrote here is correct?
Hi Daniel…Yes! You are spot on in your understanding!
Awesome,
Appreciate your quick response and amazing blog post.
What happens if using cross validation on entire data as training data set yielded an RMSE that is higher than model tested on testing data(of course assuming 80:20 split of the entire data)?
Hi Learning….the following may add clarity around underfitting and overfitting:
https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/
Hello, thank you for the explanation ! it helps me a lot. But for my models, after Hyperparameter tuning and choosing the best hyperparameters for my model, I would like to fit the model with the best parameters on the datasets (train + validation) to have a better learning and having a better estimation of the performances on the test set. But when I train the model with the train+validation dataset, how can I avoid overfitting during training if no more data are specified for the ‘validation data’ field in the .fit() method in python? Normally the training stops if the val loss stops decreasing to avoid overfitting (with a certain patience) but here how can I avoid overfitting of the model if all my available data (train+ validation) are used to trained the model ?
Thank you for your consideration =)
Hi Aurelie…The following resources will hopefully add clarity:
https://machinelearningmastery.com/early-stopping-to-avoid-overtraining-neural-network-models/
https://elitedatascience.com/overfitting-in-machine-learning
If ML model yielded 100% training set accuracy and 97.9% testing set accuracy, does it mean model overfitting?
Hi kibreab…Your scenario is indicative of overfitting. The following resource may be prove helpful:
https://machinelearningmastery.com/overfitting-machine-learning-models/
how does models get average precision from test images? Does test images have ground truths?
Hi Fattah…The following resources should add clarity:
https://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/accuracy-assessment-for-image-classification.htm#:~:text=The%20most%20common%20way%20to,data%20in%20a%20confusion%20matrix.
https://www.analyticsvidhya.com/blog/2021/06/evaluate-your-model-metrics-for-image-classification-and-detection/
Hi,Jason.Thanks for the tutorial.
I am new to ML so I do not quite understand the meaning of ‘summarize’ in skill = summarize(skills) for k-fold validation. What exactly do we do?
Hi Lee…You are very welcome! The following resource may be of interest to you:
https://machinelearningmastery.com/k-fold-cross-validation/