sklearn.pipeline.make_pipeline

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

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:

*steps : list of estimators,

memory : None, str or object with the joblib.Memory interface, optional

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.

Returns:

p : Pipeline

Examples

>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.preprocessing import StandardScaler
>>> make_pipeline(StandardScaler(), GaussianNB(priors=None))
...     
Pipeline(memory=None,
         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

Importance of Feature Scaling

Clustering text documents using k-means