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Can anyone help me? I am having hard time to know the difference between these
from sklearn.model_selection import train_test_split
from sklearn.cross_validation import train_test_split
In the documentation I saw it is written
"Split arrays or matrices into random train and test subsets"
for both of them.
Which should be used when??
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The train_test_split function of the sklearn. model_selection package in Python splits arrays or matrices into random subsets for train and test data, respectively.
cross_validation. train_test_split. Quick utility that wraps calls to check_arrays and next(iter(ShuffleSplit(n_samples))) and application to input data into a single call for splitting (and optionally subsampling) data in a oneliner. Python lists or tuples occurring in arrays are converted to 1D numpy arrays.
You should provide either train_size or test_size . If neither is given, then the default share of the dataset that will be used for testing is 0.25 , or 25 percent. random_state is the object that controls randomization during splitting. It can be either an int or an instance of RandomState .
The scikit-learn Python machine learning library provides an implementation of the train-test split evaluation procedure via the train_test_split() function. The function takes a loaded dataset as input and returns the dataset split into two subsets.
sklearn.cross_validation.train_test_split is deprecated, you should use
from sklearn.model_selection import train_test_split
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