evalml.pipelines.utils.make_pipeline(X, y, estimator, problem_type, parameters=None, sampler_name=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) – The input data of shape [n_samples, n_features]

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

  • estimator (Estimator) – Estimator for pipeline

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

  • parameters (dict) – Dictionary with component names as keys and dictionary of that component’s parameters as values. An empty dictionary or None implies using all default values for component parameters.

  • sampler_name – The name of the sampler component to add to the pipeline. Only used in classification problems. Defaults to None