Why split data into train and test in linear regression?












0












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I am wondering how train and test set works in linear regression.



If I train the data it will give me a line of best fit, say I for my train data I am using first 70% of dataset => first 70% of the line is from training set and final 30% is from unseen testing set?






machine-learning linear-regression







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    Welcome to SE.DataScience! What do you mean by "first" in "first 70% of the line"?
    $endgroup$
    – Esmailian
    yesterday


















0












$begingroup$


I am wondering how train and test set works in linear regression.



If I train the data it will give me a line of best fit, say I for my train data I am using first 70% of dataset => first 70% of the line is from training set and final 30% is from unseen testing set?






machine-learning linear-regression







share|improve this question







New contributor




h_musk is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$












  • $begingroup$
    Welcome to SE.DataScience! What do you mean by "first" in "first 70% of the line"?
    $endgroup$
    – Esmailian
    yesterday
















0












0








0





$begingroup$


I am wondering how train and test set works in linear regression.



If I train the data it will give me a line of best fit, say I for my train data I am using first 70% of dataset => first 70% of the line is from training set and final 30% is from unseen testing set?






machine-learning linear-regression







share|improve this question







New contributor




h_musk is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$




I am wondering how train and test set works in linear regression.



If I train the data it will give me a line of best fit, say I for my train data I am using first 70% of dataset => first 70% of the line is from training set and final 30% is from unseen testing set?






machine-learning linear-regression




machine-learning linear-regression






share|improve this question







New contributor




h_musk is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.











share|improve this question







New contributor




h_musk is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.









share|improve this question




share|improve this question






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h_musk is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked yesterday









h_muskh_musk

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h_musk is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






h_musk is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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  • $begingroup$
    Welcome to SE.DataScience! What do you mean by "first" in "first 70% of the line"?
    $endgroup$
    – Esmailian
    yesterday




















  • $begingroup$
    Welcome to SE.DataScience! What do you mean by "first" in "first 70% of the line"?
    $endgroup$
    – Esmailian
    yesterday


















$begingroup$
Welcome to SE.DataScience! What do you mean by "first" in "first 70% of the line"?
$endgroup$
– Esmailian
yesterday






$begingroup$
Welcome to SE.DataScience! What do you mean by "first" in "first 70% of the line"?
$endgroup$
– Esmailian
yesterday












2 Answers
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This testing is a way to asses your model performance. You can check the evaluation metrics for regression, classification and clustering on this link to scikit-learn.



Separating the data enables you to evaluate your model generalization capabilities and have an idea of how it would perform on unseen data.



Also, you can create a validation dataset (a split from the train dataset) to tune hyper-parameters and threshold/bias.




  • The test information should never be seem by the training algorithm by any chance! This might occlude over-fitting and other many bad things you don't want to happen! Check this link on Data Leakage for more information.






share|improve this answer









$endgroup$





















    0












    $begingroup$

    Not just in linear regression, Train-test split is a practice that is followed in the model building and evaluation workflow. Testing your dataset on a testing data that is totally excluded from the training data helps us find whether the model is overfitting or underfitting atleast.




    And always keep in mind - Never train on test data.



                                                          
    - https://developers.google.com/machine-learning/crash-course




    - Referances:




    1. Train/Test Split and Cross Validation in Python

    2. Evaluate the Performance Of Deep Learning Models in Keras

    3. The 7 Steps of Machine Learning






    share|improve this answer









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      2 Answers
      2






      active

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      2 Answers
      2






      active

      oldest

      votes









      active

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      active

      oldest

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      0












      $begingroup$

      This testing is a way to asses your model performance. You can check the evaluation metrics for regression, classification and clustering on this link to scikit-learn.



      Separating the data enables you to evaluate your model generalization capabilities and have an idea of how it would perform on unseen data.



      Also, you can create a validation dataset (a split from the train dataset) to tune hyper-parameters and threshold/bias.




      • The test information should never be seem by the training algorithm by any chance! This might occlude over-fitting and other many bad things you don't want to happen! Check this link on Data Leakage for more information.






      share|improve this answer









      $endgroup$


















        0












        $begingroup$

        This testing is a way to asses your model performance. You can check the evaluation metrics for regression, classification and clustering on this link to scikit-learn.



        Separating the data enables you to evaluate your model generalization capabilities and have an idea of how it would perform on unseen data.



        Also, you can create a validation dataset (a split from the train dataset) to tune hyper-parameters and threshold/bias.




        • The test information should never be seem by the training algorithm by any chance! This might occlude over-fitting and other many bad things you don't want to happen! Check this link on Data Leakage for more information.






        share|improve this answer









        $endgroup$
















          0












          0








          0





          $begingroup$

          This testing is a way to asses your model performance. You can check the evaluation metrics for regression, classification and clustering on this link to scikit-learn.



          Separating the data enables you to evaluate your model generalization capabilities and have an idea of how it would perform on unseen data.



          Also, you can create a validation dataset (a split from the train dataset) to tune hyper-parameters and threshold/bias.




          • The test information should never be seem by the training algorithm by any chance! This might occlude over-fitting and other many bad things you don't want to happen! Check this link on Data Leakage for more information.






          share|improve this answer









          $endgroup$



          This testing is a way to asses your model performance. You can check the evaluation metrics for regression, classification and clustering on this link to scikit-learn.



          Separating the data enables you to evaluate your model generalization capabilities and have an idea of how it would perform on unseen data.



          Also, you can create a validation dataset (a split from the train dataset) to tune hyper-parameters and threshold/bias.




          • The test information should never be seem by the training algorithm by any chance! This might occlude over-fitting and other many bad things you don't want to happen! Check this link on Data Leakage for more information.







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered yesterday









          Pedro Henrique MonfortePedro Henrique Monforte

          3379




          3379























              0












              $begingroup$

              Not just in linear regression, Train-test split is a practice that is followed in the model building and evaluation workflow. Testing your dataset on a testing data that is totally excluded from the training data helps us find whether the model is overfitting or underfitting atleast.




              And always keep in mind - Never train on test data.



                                                                    
              - https://developers.google.com/machine-learning/crash-course




              - Referances:




              1. Train/Test Split and Cross Validation in Python

              2. Evaluate the Performance Of Deep Learning Models in Keras

              3. The 7 Steps of Machine Learning






              share|improve this answer









              $endgroup$


















                0












                $begingroup$

                Not just in linear regression, Train-test split is a practice that is followed in the model building and evaluation workflow. Testing your dataset on a testing data that is totally excluded from the training data helps us find whether the model is overfitting or underfitting atleast.




                And always keep in mind - Never train on test data.



                                                                      
                - https://developers.google.com/machine-learning/crash-course




                - Referances:




                1. Train/Test Split and Cross Validation in Python

                2. Evaluate the Performance Of Deep Learning Models in Keras

                3. The 7 Steps of Machine Learning






                share|improve this answer









                $endgroup$
















                  0












                  0








                  0





                  $begingroup$

                  Not just in linear regression, Train-test split is a practice that is followed in the model building and evaluation workflow. Testing your dataset on a testing data that is totally excluded from the training data helps us find whether the model is overfitting or underfitting atleast.




                  And always keep in mind - Never train on test data.



                                                                        
                  - https://developers.google.com/machine-learning/crash-course




                  - Referances:




                  1. Train/Test Split and Cross Validation in Python

                  2. Evaluate the Performance Of Deep Learning Models in Keras

                  3. The 7 Steps of Machine Learning






                  share|improve this answer









                  $endgroup$



                  Not just in linear regression, Train-test split is a practice that is followed in the model building and evaluation workflow. Testing your dataset on a testing data that is totally excluded from the training data helps us find whether the model is overfitting or underfitting atleast.




                  And always keep in mind - Never train on test data.



                                                                        
                  - https://developers.google.com/machine-learning/crash-course




                  - Referances:




                  1. Train/Test Split and Cross Validation in Python

                  2. Evaluate the Performance Of Deep Learning Models in Keras

                  3. The 7 Steps of Machine Learning







                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered yesterday









                  thanatozthanatoz

                  569319




                  569319






















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