evalml.pipelines.utils.make_pipeline(X, y, estimator, problem_type, custom_hyperparameters=None)[source]
Given input data, target data, an estimator class and the problem type,

generates a pipeline class with a preprocessing chain which was recommended based on the inputs. The pipeline will be a subclass of the appropriate pipeline base class for the specified problem_type.

  • X (pd.DataFrame, ww.DataTable) – The input data of shape [n_samples, n_features]

  • y (pd.Series, ww.DataColumn) – The target data of length [n_samples]

  • estimator (Estimator) – Estimator for pipeline

  • problem_type (ProblemTypes or str) – Problem type for pipeline to generate

  • custom_hyperparameters – Dictionary of custom hyperparameters, with component name as key and dictionary of parameters as the value