What is the Difference Between Test and Validation Datasets?

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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?

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:

  1. What is a Validation Dataset by the Experts?
  2. Definitions of Train, Validation, and Test Datasets
  3. Validation Dataset is Not Enough
  4. 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.

— Gareth James, et al., Page 176, An Introduction to Statistical Learning: with Applications in R, 2013.

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.

— Max Kuhn and Kjell Johnson, Page 67, Applied Predictive Modeling, 2013

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.)

— Stuart Russell and Peter Norvig, page 709, Artificial Intelligence: A Modern Approach, 2009 (3rd edition)

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.

— Stuart Russell and Peter Norvig, page 709,Artificial Intelligence: A Modern Approach, 2009 (3rd edition)

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.

— Brian Ripley, page 354, Pattern Recognition and Neural Networks, 1996

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.).

Subject: What are the population, sample, training set, design set, validation set, and test set?

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:

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.

— Max Kuhn and Kjell Johnson, Page 78, Applied Predictive Modeling, 2013

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:

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.


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.

156 Responses to What is the Difference Between Test and Validation Datasets?

  1. Jacob Sanders July 14, 2017 at 5:11 pm #

    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 🙁

    • Jason Brownlee July 15, 2017 at 9:38 am #

      I’m sorry to hear that. Can you send me a photo of an example?

      • Zhang Yong December 14, 2017 at 9:23 pm #

        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! : (

        • Jason Brownlee December 15, 2017 at 5:32 am #

          Sorry to hear that. They are designed to be read online.

    • Franco April 2, 2018 at 5:17 pm #

      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!

    • GangLi July 2, 2018 at 11:41 pm #

      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.

      • Jason Brownlee July 3, 2018 at 6:26 am #

        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.

        • GangLi July 4, 2018 at 9:42 am #

          why “It depends on the problem”? I think it is best the three data set is splited as homogeneous as possible

          • Jason Brownlee July 4, 2018 at 2:55 pm #

            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.

    • Joe December 5, 2018 at 3:37 am #

      Try PrintFriendly https://www.printfriendly.com

  2. Milind Mahajani July 14, 2017 at 5:59 pm #

    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?

    • Jason Brownlee July 15, 2017 at 9:40 am #

      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:

  3. Helen July 26, 2017 at 6:29 am #

    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?

    • Jason Brownlee July 26, 2017 at 8:03 am #

      Yes, k-fold cross validation is an excellent way to calculate an unbiased estimate of the skill of your model on unseen data.

  4. René July 26, 2017 at 5:43 pm #

    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!

    • Jason Brownlee July 27, 2017 at 7:55 am #

      Glad to hear it.

      Thanks for the suggestion – updated.

  5. Steven August 1, 2017 at 11:52 am #

    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?


    • Jason Brownlee August 2, 2017 at 7:41 am #

      Sure, each set of parameters (param) is evaluated using k-fold cross validation.

      Does that help?

  6. Krish November 14, 2017 at 8:04 am #

    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.

    • Jason Brownlee November 14, 2017 at 10:22 am #


      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.

      • PriyaSaxena December 21, 2017 at 10:52 pm #

        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.

        • Jason Brownlee December 22, 2017 at 5:33 am #

          Generally, it is a good idea to perform the same data prep tasks on the other datasets.

          • PriyaSaxena December 22, 2017 at 9:20 pm #

            Okay. Thanks so much.

      • Maria April 18, 2019 at 8:45 am #

        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.

        • Jason Brownlee April 18, 2019 at 8:59 am #

          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.

          • Maria April 18, 2019 at 9:10 am #

            Does this mean the model keeps its state between executions?

          • Jason Brownlee April 18, 2019 at 12:20 pm #

            Not sure I follow.

            You can save your model to file and later load it and continue training or start making predictions.

    • Max December 17, 2017 at 10:53 pm #

      @Krish Awesome summary – you hit the point. Thanks for enhancing my understanding

    • Franco April 2, 2018 at 5:27 pm #

      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.

    • Rajan April 17, 2018 at 10:17 am #

      Great, thanks for the explanation

    • Wing July 29, 2018 at 4:26 pm #

      oh my god thanks for the summary

    • Bocun He September 4, 2018 at 6:39 pm #

      I wonder whether the expected result you said mean true target values of samples in test set ?

  7. Krish November 15, 2017 at 5:25 am #

    Thanks Jason!

  8. Magnus November 24, 2017 at 12:55 am #

    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.

  9. Luxferre December 4, 2017 at 1:49 am #

    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?

  10. Luxferre December 4, 2017 at 9:22 pm #


    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?

    • Jason Brownlee December 5, 2017 at 5:43 am #

      No you don’t have to split the data, the validation set can be useful to tune the parameters of a given model.

      • Luxferre December 8, 2017 at 1:13 pm #

        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 ?

        • Jason Brownlee December 8, 2017 at 2:30 pm #

          The idea of “right” really depends on your problem and your goals.

    • Franco April 2, 2018 at 5:41 pm #

      – 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.

      • Jason Brownlee April 3, 2018 at 6:31 am #

        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.

  11. Austin December 6, 2017 at 1:37 pm #

    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.

    • Jason Brownlee December 7, 2017 at 7:48 am #

      Thanks Austin.

      Which part of tuning do you need help with?

  12. Nil December 12, 2017 at 11:53 pm #

    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.

    • Jason Brownlee December 13, 2017 at 5:37 am #

      You could collect all predictions/errors across CV folds or simply evaluate the model directly on a test set.

      • Nil December 13, 2017 at 10:23 pm #

        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

        • Jason Brownlee December 14, 2017 at 5:37 am #

          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?

          • Nil December 14, 2017 at 5:59 pm #

            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.

          • Jason Brownlee December 15, 2017 at 5:30 am #

            Do the examples in this post help?

          • Nil December 15, 2017 at 8:55 pm #

            Let me read again the and the examples, maybe I can find out something I didn’t see in the first read.

            Best regards.

  13. samik January 9, 2018 at 11:14 pm #

    Hi Jason,

    What is the industry % standard to split the data into 3 data sets i.e train,validate and test ?

  14. samik January 10, 2018 at 2:05 pm #

    Do we have the industry % for splitting the data ?

    • Jason Brownlee January 10, 2018 at 3:45 pm #

      Depends on data.

      Perhaps 70 for training, 30 for test. Then same again for training into training(2) and validation.

  15. Behrouz February 14, 2018 at 7:44 am #

    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.

  16. Dana February 16, 2018 at 7:25 am #

    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.

  17. sarthak tiwari March 12, 2018 at 8:41 am #

    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.

    • Jason Brownlee March 12, 2018 at 2:24 pm #

      A validation set can be used within each fold for tuning the model.

      • sarthak tiwari March 13, 2018 at 2:31 am #

        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?

        • Jason Brownlee March 13, 2018 at 6:31 am #

          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:

          • Sarthak Tiwari March 13, 2018 at 8:06 pm #

            Okay… something like nested cross validation…but the question is whether that’s necessary. And how do I know which approach is better?

          • Jason Brownlee March 14, 2018 at 6:18 am #

            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.

          • sarthak tiwari April 3, 2018 at 3:19 am #

            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.

          • Jason Brownlee April 3, 2018 at 6:39 am #

            Sorry, I’m not sure I follow.

          • sarthak tiwari April 3, 2018 at 11:03 pm #

            Do you think it is incorrect?

          • Jason Brownlee April 4, 2018 at 6:14 am #

            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.

  18. jazz March 23, 2018 at 11:09 pm #

    is it necessary to create a test set

    • Jason Brownlee March 24, 2018 at 6:29 am #

      You must choose how you wish to run your project and estimate the skill of models.

  19. Amit March 25, 2018 at 1:21 am #

    How to add cross validation in keras model? I don’t want to use k fold

  20. myagmur April 11, 2018 at 11:09 am #

    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

  21. Tim H. May 1, 2018 at 6:59 am #

    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?

    • Jason Brownlee May 2, 2018 at 5:35 am #

      Perhaps you can use summary statistics on the datasets and compare the results using statistical tests?

  22. Yusuf Albasia May 20, 2018 at 8:11 am #

    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

    • Jason Brownlee May 21, 2018 at 6:23 am #

      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.

  23. Bernard Ong May 22, 2018 at 1:36 pm #

    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.

    • Jason Brownlee May 22, 2018 at 2:59 pm #

      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:

      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.

  24. Chris May 24, 2018 at 6:21 am #

    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.


    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?

    • Jason Brownlee May 24, 2018 at 8:23 am #

      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.

  25. Mark May 31, 2018 at 2:40 pm #

    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?

    • Jason Brownlee June 1, 2018 at 8:13 am #

      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?

  26. Sarah June 7, 2018 at 10:48 pm #

    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?

    • Jason Brownlee June 8, 2018 at 6:14 am #

      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.

      • Sarah June 26, 2018 at 5:52 pm #

        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?

        • Jason Brownlee June 27, 2018 at 8:13 am #

          Depends on the problem and the goals of the project. There is no general answer.

  27. Sharan Amutharasu June 14, 2018 at 5:17 pm #

    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?

    • Jason Brownlee June 15, 2018 at 6:43 am #

      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.

      • Sharan Amutharasu June 16, 2018 at 6:18 am #

        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.

        • Jason Brownlee June 16, 2018 at 7:33 am #

          Yes, bu we want to minimize this difference with a robust estimation via our test harness.

  28. Daniel June 22, 2018 at 8:04 pm #

    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!

    • Jason Brownlee June 23, 2018 at 6:15 am #

      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.

  29. Mahesh July 23, 2018 at 2:43 pm #

    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

  30. Sourya Dey August 10, 2018 at 4:52 am #

    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?

  31. San August 16, 2018 at 11:19 pm #

    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.

  32. Ibrahim September 2, 2018 at 6:45 pm #

    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.

    • Jason Brownlee September 3, 2018 at 6:14 am #

      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.

  33. suryad September 3, 2018 at 3:15 am #


    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

    • Jason Brownlee September 3, 2018 at 6:16 am #

      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.

  34. Mohammad September 14, 2018 at 9:09 pm #

    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


    • Jason Brownlee September 15, 2018 at 6:07 am #

      Find a split that makes sense for your domain and amount of data.

  35. John November 27, 2018 at 7:22 am #

    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

    • Jason Brownlee November 27, 2018 at 2:08 pm #

      Sounds correct to me John.

      This is just a heuristic though, a good practice. It is not a strict rule.

  36. Amit January 18, 2019 at 3:07 am #

    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

    • Jason Brownlee January 18, 2019 at 5:47 am #

      It does not matter, use the approach that you prefer.

    • Ícaro Oliveira March 31, 2019 at 9:33 am #

      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 ?

  37. Daniel Penalva January 21, 2019 at 1:57 pm #

    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

  38. Sriparna Banerjee January 24, 2019 at 10:05 pm #

    Sir I am new to this field. Can you please guide me how validation set helps in feature selection.

    • Jason Brownlee January 25, 2019 at 8:44 am #

      You can use feature selection methods using the validation dataset, which will give independent results from the test set.

      • Sriparna Banerjee January 26, 2019 at 1:41 am #

        Thank you sir. But I have one question that how can I use validation set for feature selection while using k-cross validation.

        • Jason Brownlee January 26, 2019 at 6:15 am #

          Split each train set within each fold into train/validation, fit on the train and evaluation feature selection with the validation.

  39. Lobbie February 7, 2019 at 5:42 pm #

    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?


  40. Osama March 6, 2019 at 11:32 pm #

    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?

  41. Leen April 6, 2019 at 4:36 am #

    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.

  42. Joran April 11, 2019 at 4:03 am #

    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!

    • Jason Brownlee April 11, 2019 at 6:46 am #


      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.

  43. steven ndung'u April 11, 2019 at 9:40 pm #

    Great article. Clear distinction provided. Thank you

  44. Brownie April 19, 2019 at 2:30 am #

    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?

    • Jason Brownlee April 19, 2019 at 6:18 am #

      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?

  45. Karthik May 14, 2019 at 8:24 am #

    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?

  46. A Bargla June 1, 2019 at 8:49 am #

    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?

    • Jason Brownlee June 2, 2019 at 6:35 am #

      Why do you think splitting data when developing a descriptive model would be useful?

      • AB June 4, 2019 at 11:56 pm #

        I understand, it should ideally not be there as it is not predicting anything but had some confusion in mind, so posted this question!

        • Jason Brownlee June 5, 2019 at 8:45 am #

          No problem, when developing a descriptive model, fit it on all available data.

  47. PW June 12, 2019 at 7:27 am #


    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.

    • Jason Brownlee June 12, 2019 at 8:08 am #

      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.

  48. Jannes August 8, 2019 at 5:30 pm #

    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?

    • Jason Brownlee August 9, 2019 at 8:06 am #

      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?

  49. Matti August 27, 2019 at 10:35 pm #

    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/ ?

  50. EdwinLeonardo September 26, 2019 at 8:35 pm #

    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?

  51. Debit October 1, 2019 at 1:39 am #

    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 ?

    • Jason Brownlee October 1, 2019 at 6:56 am #

      Not sure I follow, sorry.

      Perhaps test each approach and see which results in a more robust evaluation of your methods?

  52. joshnarani October 4, 2019 at 8:20 pm #

    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..

    • Jason Brownlee October 6, 2019 at 8:07 am #

      Ideally, each dataset should be representative of the broader problem.

      This is a general requirement when modeling.

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