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Why split data into train and test in linear regression?

Difference between OLS(statsmodel) and Scikit Linear RegressionHow to split train/test in recommender systemsTrain/Test Split after perform SMOTELinear Regression and k-fold cross validationBest way to normalize datasets for a linear regression model?Text classification- What to do when train and test data have different featuresTrain Test Split for overlapping samplesPartitioning data into features/labels and train/test after reading from csv fileDifference between various linear regression implementationsScikit learn train test split without mixing participants in trails

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

asked Apr 7 at 4:21

h_muskh_musk

132

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Welcome to SE.DataScience! What do you mean by "first" in "first 70% of the line"?
  $endgroup$
  – Esmailian
  Apr 7 at 12:54
  
  

  
  
  
  

add a comment | 

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

asked Apr 7 at 4:21

h_muskh_musk

132

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Welcome to SE.DataScience! What do you mean by "first" in "first 70% of the line"?
  $endgroup$
  – Esmailian
  Apr 7 at 12:54
  
  

  
  
  
  

add a comment | 

$begingroup$

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

asked Apr 7 at 4:21

h_muskh_musk

132

$endgroup$

share|improve this question

asked Apr 7 at 4:21

h_muskh_musk

132

share|improve this question

asked Apr 7 at 4:21

h_muskh_musk

132

asked Apr 7 at 4:21

h_muskh_musk

132

asked Apr 7 at 4:21

h_muskh_musk

132

2 Answers
2

active

oldest

votes

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

answered Apr 7 at 4:30

Pedro Henrique MonfortePedro Henrique Monforte

569219

$endgroup$

add a comment | 

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

answered Apr 7 at 5:10

thanatozthanatoz

709521

$endgroup$

add a comment | 

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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

answered Apr 7 at 4:30

Pedro Henrique MonfortePedro Henrique Monforte

569219

$endgroup$

add a comment | 

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

answered Apr 7 at 4:30

Pedro Henrique MonfortePedro Henrique Monforte

569219

$endgroup$

add a comment | 

$begingroup$

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

answered Apr 7 at 4:30

Pedro Henrique MonfortePedro Henrique Monforte

569219

$endgroup$

share|improve this answer

answered Apr 7 at 4:30

Pedro Henrique MonfortePedro Henrique Monforte

569219

answered Apr 7 at 4:30

Pedro Henrique MonfortePedro Henrique Monforte

569219

answered Apr 7 at 4:30

Pedro Henrique MonfortePedro Henrique Monforte

569219

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

answered Apr 7 at 5:10

thanatozthanatoz

709521

$endgroup$

add a comment | 

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

answered Apr 7 at 5:10

thanatozthanatoz

709521

$endgroup$

add a comment | 

$begingroup$

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

answered Apr 7 at 5:10

thanatozthanatoz

709521

$endgroup$

share|improve this answer

answered Apr 7 at 5:10

thanatozthanatoz

709521

answered Apr 7 at 5:10

thanatozthanatoz

709521

answered Apr 7 at 5:10

thanatozthanatoz

709521