evalml.automl.AutoSearchBase.search

AutoSearchBase.search(X, y, data_checks=None, feature_types=None, raise_errors=True, show_iteration_plot=True)

Find best classifier

Parameters

• X (pd.DataFrame) – the input training data of shape [n_samples, n_features]

• y (pd.Series) – the target training labels of length [n_samples]

• feature_types (list, optional) – list of feature types, either numerical or categorical. Categorical features will automatically be encoded

• raise_errors (boolean) – If True, raise errors and exit search if a pipeline errors during fitting. If False, set scores for the errored pipeline to NaN and continue search. Defaults to True.

• show_iteration_plot (boolean, True) – Shows an iteration vs. score plot in Jupyter notebook. Disabled by default in non-Jupyter enviroments.

• data_checks (DataChecks, None) – A collection of data checks to run before searching for the best classifier. If data checks produce any errors, an exception will be thrown before the search begins. If None, uses DefaultDataChecks. Defaults to None.

Returns

self