evalml.automl.AutoMLSearch¶

class evalml.automl.AutoMLSearch(problem_type=None, objective='auto', max_pipelines=None, max_time=None, patience=None, tolerance=None, data_split=None, allowed_pipelines=None, allowed_model_families=None, start_iteration_callback=None, add_result_callback=None, additional_objectives=None, random_state=0, n_jobs=-1, tuner_class=None, verbose=True, optimize_thresholds=False, _max_batches=None)[source]¶

Automated Pipeline search.

Methods

`__init__`	Automated pipeline search
`add_to_rankings`	Fits and evaluates a given pipeline then adds the results to the automl rankings with the requirement that automl search has been run.
`describe_pipeline`	Describe a pipeline
`get_pipeline`	Given the ID of a pipeline training result, returns an untrained instance of the specified pipeline initialized with the parameters used to train that pipeline during automl search.
`load`	Loads AutoML object at file path
`print_objective_names_allowed_in_automl`
`save`	Saves AutoML object at file path
`search`	Find the best pipeline for the data set.

Attributes

`best_pipeline`	Returns an untrained instance of the best pipeline and parameters found during automl search.
`data_check_results`
`full_rankings`	Returns a pandas.DataFrame with scoring results from all pipelines searched
`has_searched`	Returns True if search has been ran and False if not
`rankings`	Returns a pandas.DataFrame with scoring results from the highest-scoring set of parameters used with each pipeline.
`results`	Class that allows access to a copy of the results from automl_search.

evalml.preprocessing.split_data evalml.automl.AutoMLSearch.__init__