pipefitter.pipeline.Pipeline.fit

Pipeline.fit(table, *args, **kwargs)

Train the models using the stages in the pipeline

Parameters:

table : data set

Any data set object supported by the transformers and estimators in the pipeline stages

*args : positional parameters, optional

Any valid parameters to the estimators’ fit method

**kwargs : keyword parameters, optional

Any valid keyword parameters to the estimators’ fit method

Returns:

PipelineModel

Notes

Parameters passed in on this method are not persisted on the pipeline. They are only used during the scope of this method.

Examples

Basic pipeline fit using imputers and an estimator:

>>> mean_imp = Imputer(Imputer.MEAN)
>>> mode_imp = Imputer(Imputer.MODE)
>>> dtree = DecisionTree(target='Origin',
...                      nominals=['Type', 'Cylinders', 'Origin'],
...                      inputs=['MPG_City', 'MPG_Highway', 'Length',
...                              'Weight', 'Type', 'Cylinders'])
>>> pipe = Pipeline([mean_imp, mode_imp, dtree])
>>> model = pipe.fit(data)