sklearn.pipeline.make_pipeline¶

sklearn.pipeline.make_pipeline(*steps, **kwargs)[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 estimators.

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 or steps 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

See also

sklearn.pipeline.Pipeline

Class for creating a pipeline of transforms with a final estimator.

Examples

>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.preprocessing import StandardScaler
>>> make_pipeline(StandardScaler(), GaussianNB(priors=None))
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('gaussiannb', GaussianNB())])

Examples using sklearn.pipeline.make_pipeline¶

Release Highlights for scikit-learn 0.23¶

Release Highlights for scikit-learn 0.22¶

Feature transformations with ensembles of trees¶

Combine predictors using stacking¶

Pipeline Anova SVM¶

Univariate Feature Selection¶

Polynomial interpolation¶

Robust linear estimator fitting¶

Poisson regression and non-normal loss¶

Tweedie regression on insurance claims¶

Partial Dependence Plots¶

Common pitfalls in interpretation of coefficients of linear models¶

Visualizations with Display Objects¶

Advanced Plotting With Partial Dependence¶

Imputing missing values with variants of IterativeImputer¶

Imputing missing values before building an estimator¶

Dimensionality Reduction with Neighborhood Components Analysis¶

Approximate nearest neighbors in TSNE¶

Varying regularization in Multi-layer Perceptron¶

Using FunctionTransformer to select columns¶

Importance of Feature Scaling¶

Feature discretization¶

Clustering text documents using k-means¶