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

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))
...     # doctest: +NORMALIZE_WHITESPACE
Pipeline(memory=None,
         steps=[('standardscaler',
                 StandardScaler(copy=True, with_mean=True, with_std=True)),
                ('gaussiannb',
                 GaussianNB(priors=None, var_smoothing=1e-09))])

Examples using sklearn.pipeline.make_pipeline

Imputing missing values before building an estimator

Feature transformations with ensembles of trees

Pipeline Anova SVM

Polynomial interpolation

Robust linear estimator fitting

Using FunctionTransformer to select columns

Importance of Feature Scaling

Feature discretization

Clustering text documents using k-means