sklearn.pipeline.make_pipeline()

sklearn.pipeline.make_pipeline(*steps) [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.

Returns:	p : Pipeline

Examples

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

Examples using sklearn.pipeline.make_pipeline

Feature transformations with ensembles of trees

Pipeline Anova SVM

Polynomial interpolation

Robust linear estimator fitting

Using FunctionTransformer to select columns

Clustering text documents using k-means