How to Connect Model Input Data With Predictions for Machine Learning

Fitting a model to a training dataset is so easy today with libraries like scikit-learn.

A model can be fit and evaluated on a dataset in just a few lines of code. It is so easy that it has become a problem.

The same few lines of code are repeated again and again and it may not be obvious how to actually use the model to make a prediction. Or, if a prediction is made, how to relate the predicted values to the actual input values.

I know that this is the case because I get many emails with the question:

How do I connect the predicted values with the input data?

This a common problem.

In this tutorial, you will discover how to relate the predicted values with the inputs to a machine learning model.

After completing this tutorial, you will know:

  • How to fit and evaluate the model on a training dataset.
  • How to use the fit model to make predictions one at a time and in batches.
  • How to connect the predicted values with the inputs to the model.

Kick-start your project with my new book Machine Learning Mastery With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

  • Update Jan/2020: Updated for changes in scikit-learn v0.22 API.
How to Connect Model Input Data With Predictions for Machine Learning

How to Connect Model Input Data With Predictions for Machine Learning
Photo by Ian D. Keating, some rights reserved.

Tutorial Overview

This tutorial is divided into three parts; they are:

  1. Prepare a Training Dataset
  2. How to Fit a Model on the Training Dataset
  3. How to Connect Predictions With Inputs to the Model

Prepare a Training Dataset

Let’s start off by defining a dataset that we can use with our model.

You may have your own dataset in a CSV file or in a NumPy array in memory.

In this case, we will use a simple two-class or binary classification problem with two numerical input variables.

  • Inputs: Two numerical input variables:
  • Outputs: A class label as either a 0 or 1.

We can use the make_blobs() scikit-learn function to create this dataset with 1,000 examples.

The example below creates the dataset with separate arrays for the input (X) and outputs (y).

Running the example creates the dataset and prints the shape of each of the arrays.

We can see that there are 1,000 rows for the 1,000 samples in the dataset. We can also see that the input data has two columns for the two input variables and that the output array is one long array of class labels for each of the rows in the input data.

Next, we will fit a model on this training dataset.

How to Fit a Model on the Training Dataset

Now that we have a training dataset, we can fit a model on the data.

This means that we will provide all of the training data to a learning algorithm and let the learning algorithm to discover the mapping between the inputs and the output class label that minimizes the prediction error.

In this case, because it is a two-class problem, we will try the logistic regression classification algorithm.

This can be achieved using the LogisticRegression class from scikit-learn.

First, the model must be defined with any specific configuration we require. In this case, we will use the efficient ‘lbfgs‘ solver.

Next, the model is fit on the training dataset by calling the fit() function and passing in the training dataset.

Finally, we can evaluate the model by first using it to make predictions on the training dataset by calling predict() and then comparing the predictions to the expected class labels and calculating the accuracy.

The complete example is listed below.

Running the example fits the model on the training dataset and then prints the classification accuracy.

In this case, we can see that the model has a 100% classification accuracy on the training dataset.

Now that we know how to fit and evaluate a model on the training dataset, let’s get to the root of the question.

How do you connect inputs of the model to the outputs?

How to Connect Predictions With Inputs to the Model

A fit machine learning model takes inputs and makes a prediction.

This could be one row of data at a time; for example:

  • Input: 2.12309797 -1.41131072
  • Output: 1

This is straightforward with our model.

For example, we can make a prediction with an array input and get one output and we know that the two are directly connected.

The input must be defined as an array of numbers, specifically 1 row with 2 columns. We can achieve this by defining the example as a list of rows with a list of columns for each row; for example:

We can then provide this as input to the model and make a prediction.

Tying this together with fitting the model from the previous section, the complete example is listed below.

Running the example defines the new input and makes a prediction, then prints both the input and the output.

We can see that in this case, the model predicts class label 1 for the inputs.

If we were using the model in our own application, this usage of the model would allow us to directly relate the inputs and outputs for each prediction made.

If we needed to replace the labels 0 and 1 with something meaningful like “spam” and “not spam“, we could do that with a simple if-statement.

So far so good.

What happens when the model is used to make multiple predictions at once?

That is, how do we relate the predictions to the inputs when multiple rows or multiple samples are provided to the model at once?

For example, we could make a prediction for each of the 1,000 examples in the training dataset as we did in the previous section when evaluating the model. In this case, the model would make 1,000 distinct predictions and return an array of 1,000 integer values. One prediction for each of the 1,000 input rows of data.

Importantly, the order of the predictions in the output array matches the order of rows provided as input to the model when making a prediction. This means that the input row at index 0 matches the prediction at index 0; the same is true for index 1, index 2, all the way to index 999.

Therefore, we can relate the inputs and outputs directly based on their index, with the knowledge that the order is preserved when making a prediction on many rows of inputs.

Let’s make this concrete with an example.

First, we can make a prediction for each row of input in the training dataset:

We can then step through the indexes and access the input and the predicted output for each.

This shows precisely how to connect the predictions with the input rows. For example, the input at row 0 and the prediction at index 0:

In this case, we will just look at the first 10 rows and their predictions.

Tying this together, the complete example of making a prediction for each row in the training data and connecting the predictions with the inputs is listed below.

Running the example, the model makes 1,000 predictions for the 1,000 rows in the training dataset, then connects the inputs to the predicted values for the first 10 examples.

This provides a template that you can use and adapt for your own predictive modeling projects to connect predictions to the input rows via their row index.

Further Reading

This section provides more resources on the topic if you are looking to go deeper.




In this tutorial, you discovered how to relate the predicted values with the inputs to a machine learning model.

Specifically, you learned:

  • How to fit and evaluate the model on a training dataset.
  • How to use the fit model to make predictions one at a time and in batches.
  • How to connect the predicted values with the inputs to the model.

Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.

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86 Responses to How to Connect Model Input Data With Predictions for Machine Learning

  1. Avatar
    kp836453 November 22, 2019 at 6:13 pm #

    Nice Article On Machine Learning

  2. Avatar
    Tal December 5, 2019 at 5:05 am #

    Nice article continue writing

    • Avatar
      Jason Brownlee December 5, 2019 at 6:43 am #


      • Avatar
        Kareem March 20, 2021 at 4:53 pm #

        If dataset has columns containing strings. How to fit.

        • Avatar
          Jason Brownlee March 21, 2021 at 6:07 am #

          You can convert the strings to numbers. e.g. one hot encoding for categories, bag of words for free text.

  3. Avatar
    Gabriel February 6, 2020 at 9:01 am #

    Hi Jason! Have a question:

    I’m doing a logistic regression for predicting churn. I’m using the hole sample of our customers with targets “1” for churned and 0 for “lives”, my questions are:

    – I am training the model with all these customers and getting probabilities of churn as ouput, I can only apply this model to new records, right?
    – I did a lot of pre-processing and scaling methods on the training data, I need to do all these steps also to my new input and then predict right?


    • Avatar
      Jason Brownlee February 6, 2020 at 1:45 pm #

      You can use the model to make predictions on any data you like. It only makes sense to evaluate the model on new examples (a test set), and to use a final model to make predictions where you don’t know the answer.

      Yes, all operations performed on the training dataset must be performed on new data.

    • Avatar
      Tumi Sebela March 20, 2021 at 5:01 pm #

      To make things simple, use scikit pipeline to chain together all your pre-processing steps

  4. Avatar
    Mikkel Andreas Kvande April 24, 2020 at 10:14 pm #

    Hi Jason! I have a different question about connecting input and output.

    I have a neural network with 5 nodes on the output layer. The model is supose to predict a number, which can be (-2, -1, 0, 1 ,2).

    So for each prediction i get an array like this:
    [0.20181388, 0.19936344, 0.19890821, 0.19744289, 0.20247154]

    Now how do i know what number each box represents? Because it looks like after one build, train and prediction, Box 0 represents the number 1, and after another full build, train and prediction Box 0 now represents the number -2. Whats the connection here?

    Thank you!

    • Avatar
      Jason Brownlee April 25, 2020 at 6:49 am #

      If your model predicts one number per sample, then you have 5 samples worth of predictions.

      If your model predicts 5 numbers per sample, then you have 1 sample worth of predictions.

      • Avatar
        Mikkel Andreas Kvande April 28, 2020 at 5:30 pm #

        Thank you for your reply!

        Yes my model predicts one number per sample, but the array show above is just for one sample.

        I probably should have specified that, but my model outputs lets say 100 test samples after training. So then i get a list, containing 100 lists, which each again contains 5 float numbers.

        So my problem is that for each sample, i have no idea what these 5 prediction numbers represents.

        • Avatar
          Jason Brownlee April 29, 2020 at 6:22 am #

          They represent what ever you trained your model to predict.

  5. Avatar
    Pooja May 7, 2020 at 9:32 pm #


    I have prepared a Linear Regression model with X=(x1,x2,x3) and Y=y1, now I want to predict x1. How should I do it?

    • Avatar
      Jason Brownlee May 8, 2020 at 6:31 am #

      Call model.predict().

      • Avatar
        Aditya Bothra August 19, 2020 at 1:03 am #

        I have prepared a Linear Regression model with inpute feature=(x1). x1 has 300 values of datas at function of time like for date 1-1-2000 it was 250, 1-2-2000 it is 247, 1-3-2000 it is 263 AND So on.I want to predict x1 as a function of time in future. How should I do it?

  6. Avatar
    vanitha August 17, 2020 at 5:59 pm #

    Thanks. really good explanation of one important question.

  7. Avatar
    Aditya Bothra August 19, 2020 at 12:58 am #

    hi, i want to make a regression model predict on just a single column of data. 1 feature just the time series of values and i want to predict that, how would i use that in a neural network?

  8. Avatar
    Haroon khan August 19, 2020 at 7:36 pm #

    i made a model with random forest classifier predicticted my test set using 3 features from train now i want to predict new data by giving 3 new/different values for these features but getting an error
    pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

    pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

    pandas/_libs/index_class_helper.pxi in pandas._libs.index.Int64Engine._check_type()

    KeyError: ‘habitat’

    what m i doing wrong ?

  9. Avatar
    Christos Karapapas September 23, 2020 at 4:53 am #

    Another great article! I have a question, for the following scenario.

    Let’s assume that we have data with 20 features and we have already done the following:
    1) we have studied the data, 2) made all the necessary transformations 3) scaling 4) extra feature engineering 5) feature importance inspection 6) manual and/or automatic feature selection.

    And we have ended up with 5 features, so before training we use PCA to further reduce dimensionality to let’s say two components. And we finally train some models, we evaluate them and we select one for “production”.

    So, now comes the time when a client sends us a new sample to predict its class. The client has information based on the initial schema, for every sample he can get us the value for those 20 features. So we can always a list of the (5) features we used to feed PCA but I guess that our trained model would expect two values.

    So, the question is how do we recreate the effects of PCA for new samples that we want to predict their target class?

  10. Avatar
    Shivam Kumar September 25, 2020 at 5:02 am #

    if I have 3 color signals (blue, red, green) displaying one by one, how can we predict the next outcome color through machine learning? and which algorithm we needed for this?
    Please Answer……………………..

  11. Avatar
    Sriram September 26, 2020 at 1:25 am #

    Hi Jason, I developed a classification model where the target variable is multi class with a set of group names. I encoded the target variable using pd.get_dummies. Now when I test the model against a new text description, I get the output as a numeric vector. How do I convert this vector back to the group name? Can you please help?

  12. Avatar
    Thomas October 1, 2020 at 5:30 am #

    Hey man, awesome article.
    I’m just not so sure on how to use the data after the prediction.

    Because I’m not using the customer_id column on my model, as it’s a feature that can lower my model’s accuracy.
    So when I use my ‘new_input’ data to the model as a parameter, it also doesn’t have the customer_id.
    So how to know which of my customers is related to one prediction or the other?

    • Avatar
      Jason Brownlee October 1, 2020 at 6:35 am #


      Remove the customer id, but keep the array. The index (row number) of the id will match the index of an input to the model and the prediction from the model. This index will link all 3 pieces of information.

      E.g. the customer id at row 0, the input to the model at row 0, the prediction at row 0 are all linked.

      • Avatar
        Sam December 16, 2020 at 8:54 am #

        Hi Jason, amazing site, thank you!

        How do you match the customer id if you need to drop it before using pipeline to fit_resample due to imbalanced data? Doesn’t the over/under sampling mean the indexes won’t match?

  13. Avatar
    Aanchal Saraswat October 1, 2020 at 4:14 pm #

    Hey Nice article but how to predict from my own dataset? Like I have a dataset in a excel file and after applying logistic regression on the dataset I am getting an accuracy of 80%. Now I want to give some inputs and want to predict the output how should I write the code?

  14. Avatar
    Sravan November 3, 2020 at 5:24 pm #

    Is it possible to write single code for all type of datasets .I mean irrespective of the data(classification, regression, categorical, missing, normalization….etc). The code is independent on dataset work on every data that is given,..

  15. Avatar
    Israel March 11, 2021 at 8:45 am #

    Hello Dear,
    I am working on a sensor device that acquires datasets from sample substances and with machine learning algorithm I predict the substance. I am using Decision Tree of Scikit Learn. But I noticed that if I acquire datasets that have very close values, the sensor finds it difficult to accurately differentiate between the datesets and so it begins to make wrong predictions. For instance if I acquire datasets such as the ones below:


    and I test the sensor with a dataset such as


    instead of predicting Carton, it could predict Tissue or Paper.

    In this situation, what should I do to improve on its ability to differentiate between such values? Is there an algorithm I need to employ?
    Thank you.

  16. Avatar
    akurt March 17, 2021 at 1:19 am #

    Thank you for this great article.
    What if I scaled my dataset? Should I provide the input in raw data format or should I scale it first? In my case, my model fails when provide my input in raw data format but when I provide a scaled form it can predict successfully.

    • Avatar
      Jason Brownlee March 17, 2021 at 6:09 am #

      Evaluate the performance of your model with and without scaling and use the approach that works the best.

  17. Avatar
    Priyanka March 20, 2021 at 12:53 am #

    Hi Jason,

    Thanks for your post.. good read
    Any idea how to link model with the GUI created in Tkinter for predicting output basis input shared through GUI

    • Avatar
      Jason Brownlee March 20, 2021 at 5:24 am #

      Sounds like a software engineering question, perhaps you can post your code and question to stackoverflow.

  18. Avatar
    Ganpat Patel March 24, 2021 at 8:34 pm #

    Hi Jason
    Great article!
    I had a doubt, it would be really nice of you if you help me out.

    I am predicting the “User Rating” of books. In this model, the key feature can be the “Author” name. Now author name is in a string. So, first i used Label Encoder and it assigned each author name with a unique number. But now, when i take the new input to which i want to predict the value. The problem is that, author name will be a “string” in the input. So now how will i know that which number is assigned to that particular author when i fitted the model?
    To get rid out of thus problem, i first add my new input features in my existing dataframe and then again i used the Label Encoder. But, in this approach i get my prediction wrong if the author is a new one who do not have any historical data. The Label encoder alters the numbers assigned to different names when we use it 2nd time after adding our input.

    What can be the approach to solve such problems?
    please help…

    • Avatar
      Jason Brownlee March 25, 2021 at 4:43 am #

      Some ideas to explore:

      Perhaps you can prepare all known authors in your domain?
      Perhaps you can use a bag of words encoding?
      Perhaps you can use a word embedding for authors?
      Perhaps you can add to your encoding as new authors are encountered?
      Perhaps you can remove author from your model or confirm that it is predictive?
      Perhaps you can check the literature for common solutions?

  19. Avatar
    Anass March 26, 2021 at 11:22 am #

    hi Jason

    thank you very much for your awsome courses.

    I kindely ask for some help.

    I have trained a machine learning model on multiple embeddings at a time and i want to test on just one embedding. The input shape of my model is (?, 10, 300) 10 embeddings with a dimension of 300. and I want to test on (?, 1, 300).

    What can be the approach to solve such problems?
    please help me.

    • Avatar
      Jason Brownlee March 29, 2021 at 5:49 am #

      You’re welcome.

      Sorry, I don’t understand why you would have a separate embedding for train and test?

  20. Avatar
    Sara May 28, 2021 at 4:28 am #

    Hi Jason,
    I have a problem fitting a Machine learning model (BoostedTreesRegressor) to input features where the input features contain variables that must be trained (optimized) during training. I do not mean hyper parameters, I mean variables that gradually change similar to weights/bias in a neural network for example.
    I think sklearn libraries are like a black box that only train the model parameters and there is no way to train additional variables or parameters . While, in Tensor flow 1, by using tf.Variable and we can train variables addition to regular model variables.
    However, I can not find any way to use tf.Variable with BoostedTreesRegressor in tensor flow.
    what do you think about possibility of training extra variables in python libraries? any help would be highly appreciated. Sara

    • Avatar
      Jason Brownlee May 28, 2021 at 6:48 am #

      Perhaps fit a new model each time the data changes to overcome the drift in concepts.

  21. Avatar
    Abdul Munem May 30, 2021 at 5:03 am #

    Hello Jason, great explanation but I have a bit of a weird question

    I am training a credit card fraud detection model using a dataset from kaggle that is really popular with this kind of project, it has 31 features and only 3 of them are known which are: time elapsed since the first transaction in the dataset, amount of money, and transaction class (whether it’s fraud or legit) and there are 28 other features that the dataset author said are reduced using PCA to preserve user confidentiality. Thing is, the program I aim to connect this model to has fewer input features and I don’t know how to use my model to predict this.

    This is for a university project so that’s why our app dataset that contains transaction info isn’t as packed as a real-life transaction would be, it also doesn’t help that the 28 aforementioned features are unknown for confidentiality reasons, any suggestions to handle this?

    • Avatar
      Jason Brownlee May 30, 2021 at 5:54 am #


      Not sure I completely understand, sorry.

      Typically a model must be tailored to the data available. If it was prepared with different data (different number of features), it is probably not appropriate.

      Perhaps you can use transfer learning if the model and new data are related.
      Perhaps you can impute missing values.

  22. Avatar
    Priyanshu July 15, 2021 at 5:11 pm #

    Hi Jason, Great article
    I have a question that how can we separate the values that we are getting for predictions like [ 0.23.77] I want [.23] in a separate column and [.77] in a different column. The reason being I want to train a new model based on the output predictions.

    • Avatar
      Jason Brownlee July 16, 2021 at 5:22 am #

      Perhaps you can round your values?
      Perhaps you can use formatting to limit the precision when the values are displayed?

  23. Avatar
    Timothy J Fisher July 22, 2021 at 9:10 am #

    Is there any way in R, to match the True Positive results back to the original data set in order to further examine attributes about that segment that was predicted correctly? I am using H2O and the H2O frames appear to drop unique identifiers that would enable a join back to the original training data set that contains the attributes I’d like to examine further.

    Any suggestions?

    • Avatar
      Jason Brownlee July 23, 2021 at 5:42 am #

      Yes, you will ned to write some custom code for this analysis.

  24. Avatar
    Anamika August 10, 2021 at 4:02 pm #

    Thanks for sharing above information, It’s really helpful . Can you please tell me if there is any way by which after fitting the input to our predicted model. We can calculate accuracy along with output for each input value. Like in above example
    [-1.31478732 -8.78528161] 0
    [ 1.57989896 -1.462412 ] 1
    [ 1.36989667 -1.3964704 ] 1

    I want to predict accuracy for each input data ?

  25. Avatar
    mimiya August 23, 2021 at 4:38 am #

    perfect work , thank you.

    how can i add a column ” prediction” which contain the prediction result to the test set .

    • Avatar
      Adrian Tam August 23, 2021 at 5:20 am #

      May be you can get the prediction first, and then use numpy.hstack() function to “add” the column

  26. Avatar
    Ashok September 13, 2021 at 4:57 am #

    I have encoded features and target values using Count CountVectorizer and LabelEncoder respectively. After the prediction of validation dataset. How do i convert predicted target column values back to original values and concatenate with feature column?
    pls help.
    Thanks in advance.

    • Avatar
      Adrian Tam September 14, 2021 at 1:41 pm #

      LabelEncoder has the inverse_transform() function that can help you.

  27. Avatar
    Ashok September 16, 2021 at 3:22 am #

    Thank you Adrian.

  28. Avatar
    Ahmet November 24, 2021 at 9:19 am #


    I have a dataset with around 40,000 rows and with 3 attribute and 1 class attribute that I make predictions on and rows are ordered according to that class attribute (0 to 20). I can make predictions one by one row. My question is;

    How can I make predictions after looking 5 by 5 rows? I don’t want prediction for each rows, I want to see predictions after looking 5 batched rows. That means 40,000/5=8,000 predictions and calculate accuracy according to these predictions.

    Please help…

    • Avatar
      Adrian Tam November 24, 2021 at 1:16 pm #

      No, you still run accuracy row by row. That’s how accuracy is supposed to be calculated. You can run by batch, however. Each time you run “model.predict(X)” you can pass in a batch as the array X

  29. Avatar
    keyvan December 3, 2021 at 10:04 am #

    hi Adrian
    I have a question. I have a dataset that one feature of any sample is string and another features are numeric and target is numeric too. I change the string feature to a numeric using OrdinalEncoder and train my model.(Random Forest Regression). but now i want to predict a new input but when i give model new input(one feature string and another numeric) python give me an error: ‘could not convert string to float’
    can you help me plz?

    • Avatar
      Adrian Tam December 8, 2021 at 6:49 am #

      What code give you that error?

  30. Avatar
    Georgy June 27, 2022 at 4:11 am #

    Hi Adrain,

    I am working on a Human activity Classification Model which takes in real time data and predicts and counts the number of times an activty is performed. While training and testing we take in the data from csv. So, since its a time series model, I was planning to do a sliding window for segmentation. I am stuck on understanding how would this be implemented in real time when data comes in from acclerometers. Do I have to store them first to a similar csv and then take the required samples or is there another way to do this in python.

    Also, if you could guide me to any sources or materials which implements something same I would be really grateful.

  31. Avatar
    Tom Lu June 29, 2022 at 8:54 am #

    Hello Jason, great article. I really enjoy it.

    A quick question: If I have a very large number of X[i] and yhat[i], say i = 20,000 and i wish to export the results to an Excel file instead rather than using print(X[i], yhat[i]) function, how do I code that?

    Your help is greatly appreciated.

  32. Avatar
    zandel August 13, 2022 at 1:15 am #

    Thank you so much, spent the evening looking for this answer. (I’m a beginner as far as ML). Haven’t tried your code just yet but it looks good to me so I didn’t wanna wait to say thank you. Btw doesn’t anyone else think it is kinda funny how all the tutorials on the net explain ALMOST everything about predict() except this “little” thing which is actually essential when you want to use the predictions.

    • Avatar
      James Carmichael August 13, 2022 at 6:02 am #

      You are very welcome zandel! Thank you for your support and feedback! We greatly appreciate it.

  33. Avatar
    zandel August 14, 2022 at 2:09 pm #

    Could you please help me about the following, not necessarily the whole solution, a pointer to a good article would suffice. Once we have a model that produces good predictions we may want to forget about training and test data and just focus on the data that we want predictions for. I am not even sure what is a proper term, let’s call it “working data” here. So for example tomorrow there will be Monday’s data and I want Monday’s predictions, on Tuesday there will be Tuesday’s data and so on. Of course I would know how to read in the new csv file every day but after that step all examples I found talk about splitting into train and test and I don’t want to do it every time.

  34. Avatar
    Brian October 27, 2022 at 2:20 pm #

    Thank you for a great article. I used this technique to test one of my M/L models. Sadly when I provide new input (same as the first row of the test set) as your method suggests It predicts inaccurately. I am stumped.

  35. Avatar
    Brian October 28, 2022 at 5:20 pm #

    Thanks James, I am going to look into it more deeply. I actually think I AM getting accurate results, but everything was jumbled in my mind that I may have been confused. Your help is greatly appreciated. I think I will get it sorted out. Thanks!!

  36. Avatar
    Tabitha November 14, 2022 at 8:16 pm #

    Great article. I was trying this with Numpy array.
    x = np.array([121.75784416 ,124.20093101,116.35841785,108.6051847 , 95.61268754,
    77.59019338 ,50.31384723, 23.38073107 , 1.46549092 , -8.56279478,
    -13.12810052 ,-14.60476827 ,-14.54182944, -12.89880915 ,-10.53529561,
    -6.77584712 , -4.21031351 , -1.80700285 , 0.5323695 , 2.84602143,
    5.14464137 , 5.2340063 , 5.67851523 , 5.75220116 , 6.42275109,
    7.13850903 , 7.78268196 , 8.22157889 , 8.40510482 , 7.69649975,
    7.95839569 , 7.23596462 , 7.71076855 , 6.82815448 , 7.11350441,
    6.96096235 , 6.35443428 , 6.16953221 , 4.97815714 , 5.26981308,
    4.57670701 , 5.54436694])
    I am not quite sure on how to intialize the output that is y, I tried to retain the y as
    y = make_blobs(n_samples=1000, centers=2, n_features=2, random_state=2)
    But I am getting an error i.e.,’tuple’ object has no attribute ‘shape’
    Kindly, help me resolve this.

    • Avatar
      James Carmichael November 15, 2022 at 7:51 am #

      Hi Tabitha…

      Thanks for asking.

      I’m eager to help, but I just don’t have the capacity to debug code for you.

      I am happy to make some suggestions:

      Consider aggressively cutting the code back to the minimum required. This will help you isolate the problem and focus on it.
      Consider cutting the problem back to just one or a few simple examples.
      Consider finding other similar code examples that do work and slowly modify them to meet your needs. This might expose your misstep.
      Consider posting your question and code to StackOverflow.

  37. Avatar
    Mohammad Aleem April 22, 2023 at 6:40 pm #

    Hi Jason B,

    I am developing a logistic regression model where input data has a lot of categorical variables as predictors. I used one-hot encoding to get the numerical values for those columns using get_dummies function. The model works fine in predicting outcomes for training and test data, which was created from the dataset using train_test_split() function of sklearn library. Now, I am trying to use a single line/row of input for prediction and cannot run all the steps I ran for the training set of data as get_dummies returns 0 rows for 1 row of data. How do I achieve categorical to numerical conversion for one row of data? Please show an example of doing this. Thanks for the nice article.

  38. Avatar
    Imene August 8, 2023 at 4:12 am #


    Thank you very much for this interesting topic.

    Please, how can we relate the input data with their prediction after a 5-fold cross validation ? For example, if we use the following instruction:

    predicted_labels = cross_val_predict(model, texts, labels, cv=5) # texts are our data since it is classification of text using BERT.

    Is is possible to put the texts, labels and predicted label all together by using the following code :

    predictions_data = {
    “Text”: texts,
    “Gold_Label”: labels,
    “Predicted_Label”: predicted_labels,

    predictions_with_data_new_order = pd.DataFrame(predictions_data)

    My worries are that texts and labels preserve their original order, and hence it would be incorrect to match them with predicted_label that may have a different order due to shuffling data is cross validation.

  39. Avatar
    Rohit September 19, 2023 at 11:19 pm #

    I am working on the Power consumption and production dataset.

    I want to predict consumption for each of the customers(say 10) available in the system for the next time stamp.

    How can I get the predictions for each of the customers? Also, add these predicted or actual values of current timestamps back to the model trained on the input dataset.

  40. Avatar
    Rohit September 19, 2023 at 11:21 pm #

    Hi, Thanks for the article.

    I am working on the Power consumption and production dataset.

    I want to predict consumption for each of the customers(say 10) available in the system for the next time stamp.

    How can I get the predictions for each of the customers? Also, add these predicted or actual values of current timestamps back to the model trained on the input dataset.

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