evalml.automl.AutoMLSearch

class evalml.automl.AutoMLSearch(X_train=None, y_train=None, problem_type=None, objective='auto', max_iterations=None, max_time=None, patience=None, tolerance=None, data_splitter=None, allowed_pipelines=None, allowed_model_families=None, start_iteration_callback=None, add_result_callback=None, error_callback=None, additional_objectives=None, random_state=0, n_jobs=- 1, tuner_class=None, optimize_thresholds=False, ensembling=False, max_batches=None, problem_configuration=None, train_best_pipeline=True, pipeline_parameters=None, _pipelines_per_batch=5)

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
save	Saves AutoML object at file path
search	Find the best pipeline for the data set.

Attributes

best_pipeline	Returns a trained instance of the best pipeline and parameters found during automl search.
data_check_results	If there are data checks, return any error messages that are found
full_rankings	Returns a pandas.DataFrame with scoring results from all pipelines searched
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.

Class Inheritance

evalml.exceptions.NullsInColumnWarning evalml.automl.AutoMLSearch.__init__