del_ain_test_split

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sklearn.model_selection.train_test_split(*arrays, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=None)[source]#

Split arrays or matrices into random train and test subsets.

Quick utility that wraps input validation, next(ShuffleSplit().split(X, y)), and application to input data into a single call for splitting (and optionally subsampling) data into a one-liner.

Read more in the User Guide.

Parameters: *arrayssequence of indexables with same length / shape[0]

Allowed inputs are lists, numpy arrays, scipy-sparse matrices or pandas dataframes.

test_sizefloat or int, default=None

If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is set to the complement of the train size. If train_size is also None, it will be set to 0.25.

train_sizefloat or int, default=None

If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size.

random_stateint, RandomState instance or None, default=None

Controls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls. See Glossary.

shufflebool, default=True

Whether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.

stratifyarray-like, default=None

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

Returns: splittinglist, length=2 * len(arrays)

List containing train-test split of inputs.

Added in version 0.16: If the input is sparse, the output will be a scipy.sparse.csr_matrix. Else, output type is the same as the input type.

Examples

>>> importnumpyasnp >>> fromsklearn.model_selectionimport train_test_split >>> X, y = np.arange(10).reshape((5, 2)), range(5) >>> X array([[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]) >>> list(y) [0, 1, 2, 3, 4] >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, test_size=0.33, random_state=42) ... >>> X_train array([[4, 5], [0, 1], [6, 7]]) >>> y_train [2, 0, 3] >>> X_test array([[2, 3], [8, 9]]) >>> y_test [1, 4] >>> train_test_split(y, shuffle=False) [[0, 1, 2], [3, 4]] >>> fromsklearnimport datasets >>> iris = datasets.load_iris(as_frame=True) >>> X, y = iris['data'], iris['target'] >>> X.head() sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) 0 5.1 3.5 1.4 0.2 1 4.9 3.0 1.4 0.2 2 4.7 3.2 1.3 0.2 3 4.6 3.1 1.5 0.2 4 5.0 3.6 1.4 0.2 >>> y.head() 0 0 1 0 2 0 3 0 4 0 ... >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, test_size=0.33, random_state=42) ... >>> X_train.head() sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) 96 5.7 2.9 4.2 1.3 105 7.6 3.0 6.6 2.1 66 5.6 3.0 4.5 1.5 0 5.1 3.5 1.4 0.2 122 7.7 2.8 6.7 2.0 >>> y_train.head() 96 1 105 2 66 1 0 0 122 2 ... >>> X_test.head() sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) 73 6.1 2.8 4.7 1.2 18 5.7 3.8 1.7 0.3 118 7.7 2.6 6.9 2.3 78 6.0 2.9 4.5 1.5 76 6.8 2.8 4.8 1.4 >>> y_test.head() 73 1 18 0 118 2 78 1 76 1 ...

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