dtoolkit.pipeline.make_pipeline#

dtoolkit.pipeline.make_pipeline(*steps: list[dtoolkit.transformer.base.Transformer], memory=None, verbose: bool = False) → Pipeline[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.

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