This is documentation for an old release of Scikit-learn (version 1.3). Try the latest stable release (version 1.6) or development (unstable) versions.
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. 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
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
¶
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A demo of K-Means clustering on the handwritten digits data
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Principal Component Regression vs Partial Least Squares Regression
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One-Class SVM versus One-Class SVM using Stochastic Gradient Descent
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Common pitfalls in the interpretation of coefficients of linear models
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Partial Dependence and Individual Conditional Expectation Plots
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Scalable learning with polynomial kernel approximation
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Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…
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Comparing anomaly detection algorithms for outlier detection on toy datasets
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Imputing missing values before building an estimator
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Imputing missing values with variants of IterativeImputer
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Dimensionality Reduction with Neighborhood Components Analysis