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sklearn.naive_bayes.BernoulliNB

sklearn.naive_bayes.BernoulliNB

Visibility: public Uploaded 05-04-2023 by Rhonda Barnett sklearn==1.2.2 numpy>=1.17.3 scipy>=1.3.2 joblib>=1.1.1 threadpoolctl>=2.0.0 0 runs
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  • openml-python python scikit-learn sklearn sklearn_1.2.2
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Naive Bayes classifier for multivariate Bernoulli models. Like MultinomialNB, this classifier is suitable for discrete data. The difference is that while MultinomialNB works with occurrence counts, BernoulliNB is designed for binary/boolean features.

Parameters

alphaAdditive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=True, for no smoothing)default: 1.0
binarizeThreshold for binarizing (mapping to booleans) of sample features If None, input is presumed to already consist of binary vectorsdefault: 0.0
class_priorPrior probabilities of the classes. If specified, the priors are not adjusted according to the data.default: null
fit_priorWhether to learn class prior probabilities or not If false, a uniform prior will be useddefault: true
force_alphaIf False and alpha is less than 1e-10, it will set alpha to 1e-10. If True, alpha will remain unchanged. This may cause numerical errors if alpha is too close to 0 .. versionadded:: 1.2 .. deprecated:: 1.2 The default value of `force_alpha` will change to `True` in v1.4default: "warn"

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