sklearn_pmml_model.naive_bayes

The sklearn_pmml_model.naive_bayes module implements Naive Bayes algorithms. These are supervised learning methods based on applying Bayes’ theorem with strong (naive) feature independence assumptions.

Submodules

• sklearn_pmml_model.naive_bayes.implementations

Package Contents

Classes

PMMLGaussianNB

Gaussian Naive Bayes classifier.

class sklearn_pmml_model.naive_bayes.PMMLGaussianNB(pmml)

Bases: sklearn_pmml_model.base.OneHotEncodingMixin, sklearn_pmml_model.base.PMMLBaseClassifier, sklearn.naive_bayes.GaussianNB

Gaussian Naive Bayes classifier.

Can perform online updates to model parameters via partial_fit(). For details on algorithm used to update feature means and variance online, see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:

http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf

Parameters:

pmmlstr, object

Filename or file object containing PMML data.

Notes

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

_get_target_values(inputs, target)

fit(x, y)

Not supported: PMML models are already fitted.

_more_tags()