For classification you can use the stratify parameter:

stratify: array-like or None (default=None)

 

If not None, data is split in a stratified fashion, using this as the class labels.

See sklearn.model_selection.train_test_split. For example:

x, x_test, y, y_test = train_test_split(xtrain,labels,test_size=0.2, stratify=labels) 

This will ensure the class distribution is similar between train and test data. (side note: I have tossed the train_size parameter since it will be automatically determined based on test_size)

For regression there is, to my knowledge, no current implementation in scikit learn. But you can find a discussion and manual implementation here and here with regards to cross-validation.

For classification you can use the stratify parameter:

stratify: array-like or None (default=None)

If not None, data is split in a stratified fashion, using this as the class labels.

See sklearn.model_selection.train_test_split. For example:

x, x_test, y, y_test = train_test_split(xtrain,labels,test_size=0.2, stratify=labels) 

This will ensure the class distribution is similar between train and test data. (side note: I have tossed the train_size parameter since it will be automatically determined based on test_size)

For regression there is, to my knowledge, no current implementation in scikit learn. But you can find a discussion and manual implementation here and here with regards to cross-validation.

For classification you can use the stratify parameter:

stratify array-like or None (default=None)

If not None, data is split in a stratified fashion, using this as the class labels.

See sklearn.model_selection.train_test_split. For example:

x, x_test, y, y_test = train_test_split(xtrain,labels,test_size=0.2, stratify=labels) 

This will ensure the class distribution is similar between train and test data. (side note: I have tossed the train_size parameter since it will be automatically determined based on test_size)

For regression there is, to my knowledge, no current implementation in scikit learn. But you can find a discussion and manual implementation here and here with regards to cross-validation.

For classification you can use the stratify parameter:

stratify: array-like or None (default=None)

If not None, data is split in a stratified fashion, using this as the class labels.

See sklearn.model_selection.train_test_split. For example:

x, x_test, y, y_test = train_test_split(xtrain,labels,test_size=0.2, stratify=labels) 

This will ensure the class distribution is similar between train and test data. (side note: I have tossed the train_size parameter since it will be automatically determined based on test_size)

For regression there is, to my knowledge, no current implementation in scikit learn. But you can find a discussion and manual implementation here and here with regards to cross-validation.