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#
Package Contents#
Classes#
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:- 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()#