evalml.automl.AutoMLSearch.search

AutoMLSearch.search(X, y, data_checks='auto', feature_types=None, show_iteration_plot=True)

Find the best pipeline for the data set.

Parameters

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

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

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

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

• data_checks (DataChecks, list(Datacheck), str, None) – A collection of data checks to run before automl search. If data checks produce any errors, an exception will be thrown before the search begins. If “disabled” or None, no data checks will be done. If set to “auto”, DefaultDataChecks will be done. Default value is set to “auto”.

Returns

self

evalml.automl.AutoMLSearch.save evalml.automl.get_default_primary_search_objective