`sklearn.pipeline`.make_pipeline¶

sklearn.pipeline.make_pipeline(*steps, memory=None, verbose=False)[source]¶

Construct a Pipeline from the given estimators.

This is a shorthand for the Pipeline constructor; it does not require, and does not permit, naming the estimators. Instead, their names will be set to the lowercase of their types automatically.

Parameters

*stepslist of Estimator objects: List of the scikit-learn estimators that are chained together.
memorystr or object with the joblib.Memory interface, default=None: Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute named_steps or steps to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming.
verbosebool, default=False: If True, the time elapsed while fitting each step will be printed as it is completed.

Returns

pPipeline: Returns a scikit-learn Pipeline object.

See also

Pipeline: Class for creating a pipeline of transforms with a final estimator.

Examples

>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.pipeline import make_pipeline
>>> make_pipeline(StandardScaler(), GaussianNB(priors=None))
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('gaussiannb', GaussianNB())])