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sklearn.naive_bayes.MultinomialNB

sklearn.naive_bayes.MultinomialNB

Visibility: public Uploaded 05-04-2023 by Takeaki Sakabe 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 multinomial models. The multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work.

Parameters

alphaAdditive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=True, for no smoothing)default: 1.0
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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