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
orsteps
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())])
Examples using sklearn.pipeline.make_pipeline
¶
Release Highlights for scikit-learn 1.1
Release Highlights for scikit-learn 1.0
Release Highlights for scikit-learn 0.24
Release Highlights for scikit-learn 0.23
Release Highlights for scikit-learn 0.22
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