dtoolkit.pipeline.make_pipeline#
- dtoolkit.pipeline.make_pipeline(*steps: list[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())])