Parameter "stratify" from method "train_test_split" (scikit Learn)

Question

This stratify parameter makes a split so that the proportion of values in the sample produced will be the same as the proportion of values provided to parameter stratify.

For example, if variable y is a binary categorical variable with values 0 and 1 and there are 25% of zeros and 75% of ones, stratify=y will make sure that your random split has 25% of 0's and 75% of 1's.

For my future self who comes here via Google:

train_test_split is now in model_selection, hence:

from sklearn.model_selection import train_test_split

# given:
# features: xs
# ground truth: ys

x_train, x_test, y_train, y_test = train_test_split(xs, ys,
                                                    test_size=0.33,
                                                    random_state=0,
                                                    stratify=ys)

is the way to use it. Setting the random_state is desirable for reproducibility.

Scikit-Learn is just telling you it doesn't recognise the argument "stratify", not that you're using it incorrectly. This is because the parameter was added in version 0.17 as indicated in the documentation you quoted.

So you just need to update Scikit-Learn.

In this context, stratification means that the train_test_split method returns training and test subsets that have the same proportions of class labels as the input dataset.

Try running this code, it "just works":

from sklearn import cross_validation, datasets 

iris = datasets.load_iris()

X = iris.data[:,:2]
y = iris.target

x_train, x_test, y_train, y_test = cross_validation.train_test_split(X,y,train_size=.8, stratify=y)

y_test

array([0, 0, 0, 0, 2, 2, 1, 0, 1, 2, 2, 0, 0, 1, 0, 1, 1, 2, 1, 2, 0, 2, 2,
       1, 2, 1, 1, 0, 2, 1])