sklearn.pipeline.make_pipeline

sklearn.pipeline.make_pipeline(*steps)

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, they will be given names automatically based on their types.

Returns:p : Pipeline

Examples

>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.preprocessing import StandardScaler
>>> make_pipeline(StandardScaler(), GaussianNB())    
Pipeline(steps=[('standardscaler',
                 StandardScaler(copy=True, with_mean=True, with_std=True)),
                ('gaussiannb', GaussianNB())])

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