make_pipeline

sklearn.pipeline.make_pipeline(*steps, memory=None, transform_input=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. The last step will never be cached, even if it is a transformer. 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.

transform_inputlist of str, default=None

This enables transforming some input arguments to fit (other than X) to be transformed by the steps of the pipeline up to the step which requires them. Requirement is defined via metadata routing. This can be used to pass a validation set through the pipeline for instance.

You can only set this if metadata routing is enabled, which you can enable using sklearn.set_config(enable_metadata_routing=True).

Added in version 1.6.

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.

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())])