`sklearn_pmml_model.ensemble.forest`#

Module Contents#

Classes#

`PMMLForestClassifier`	A random forest classifier.
`PMMLForestRegressor`	A random forest regressor.

class sklearn_pmml_model.ensemble.forest.PMMLForestClassifier(pmml, n_jobs=None)#

Bases: sklearn_pmml_model.base.IntegerEncodingMixin, sklearn_pmml_model.base.PMMLBaseClassifier, sklearn.ensemble.RandomForestClassifier

A random forest classifier.

A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.

The PMML model consists out of a <Segmentation> element, that contains various <Segment> elements. Each segment contains it’s own <TreeModel>. For Random Forests, only segments with a <True/> predicate are supported.

Parameters:

pmmlstr, object: Filename or file object containing PMML data.
n_jobsint or None, optional (default=None): The number of jobs to run in parallel for the predict method. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

Notes

Specification: http://dmg.org/pmml/v4-3/MultipleModels.html

fit(x, y)#: Not supported: PMML models are already fitted.

_more_tags()#

class sklearn_pmml_model.ensemble.forest.PMMLForestRegressor(pmml, n_jobs=None)#

Bases: sklearn_pmml_model.base.IntegerEncodingMixin, sklearn_pmml_model.base.PMMLBaseRegressor, sklearn.ensemble.RandomForestRegressor

A random forest regressor.