evalml.preprocessing.split_data

evalml.preprocessing.split_data(X, y, problem_type, problem_configuration=None, test_size=0.2, random_state=None, random_seed=0)

Splits data into train and test sets.

Parameters

• X (ww.DataTable, pd.DataFrame or np.ndarray) – data of shape [n_samples, n_features]

• y (ww.DataColumn, pd.Series, or np.ndarray) – target data of length [n_samples]

• problem_type (str or ProblemTypes) – type of supervised learning problem. see evalml.problem_types.problemtype.all_problem_types for a full list.

• problem_configuration (dict) – Additional parameters needed to configure the search. For example, in time series problems, values should be passed in for the gap and max_delay variables.

• test_size (float) – What percentage of data points should be included in the test set. Defaults to 0.2 (20%).

• random_state (None, int) – Deprecated - use random_seed instead.

• random_seed (int) – Seed for the random number generator. Defaults to 0.

Returns

Feature and target data each split into train and test sets

Return type

ww.DataTable, ww.DataTable, ww.DataColumn, ww.DataColumn

evalml.preprocessing.number_of_features evalml.exceptions.MethodPropertyNotFoundError