evalml.AutoClassifier.__init__

AutoClassifier.__init__(objective=None, multiclass=False, max_pipelines=None, max_time=None, model_types=None, cv=None, tuner=None, detect_label_leakage=True, start_iteration_callback=None, add_result_callback=None, additional_objectives=None, random_state=0, verbose=True)[source]

Automated classifier pipeline search

Parameters

  • objective (Object) – the objective to optimize

  • multiclass (bool) – If True, expecting multiclass data. By default: False.

  • max_pipelines (int) – Maximum number of pipelines to search. If max_pipelines and max_time is not set, then max_pipelines will default to max_pipelines of 5.

  • max_time (int, str) – Maximum time to search for pipelines. This will not start a new pipeline search after the duration has elapsed. If it is an integer, then the time will be in seconds. For strings, time can be specified as seconds, minutes, or hours.

  • model_types (list) – The model types to search. By default searches over all model_types. Run evalml.list_model_types(“classification”) to see options.

  • cv – cross validation method to use. By default StratifiedKFold

  • tuner – the tuner class to use. Defaults to scikit-optimize tuner

  • detect_label_leakage (bool) – If True, check input features for label leakage and warn if found. Defaults to true.

  • start_iteration_callback (callable) – function called before each pipeline training iteration. Passed two parameters: pipeline_class, parameters.

  • add_result_callback (callable) – function called after each pipeline training iteration. Passed two parameters: results, trained_pipeline.

  • additional_objectives (list) – Custom set of objectives to score on. Will override default objectives for problem type if not empty.

  • random_state (int) – the random_state

  • verbose (boolean) – If True, turn verbosity on. Defaults to True