CategoricalNB - #1985
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Adds CategoricalNB, a Naive Bayes classifier for categorical features that maintains per-class frequency counts for every value of every feature and predicts with Laplace (additive) smoothing. Supports both online mode (learn_one/predict_one) and mini-batch mode (learn_many/predict_many) on any narwhals-supported eager dataframe, mirroring sklearn's CategoricalNB. Results match sklearn.naive_bayes.CategoricalNB. Signed-off-by: Lanre Shittu <136805224+Shizoqua@users.noreply.github.com> Signed-off-by: Shizoqua <136805224+Shizoqua@users.noreply.github.com>
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CategoricalNB
Fixes #1399
Summary
Adds
naive_bayes.CategoricalNB, a Naive Bayes classifier for categorical features. For each class it keeps frequency counts of every value of every feature; prediction uses the joint log-likelihood with additive (Laplace) smoothing, matchingsklearn.naive_bayes.CategoricalNB.Features
learn_one/predict_onefor online learninglearn_many/predict_manyfor mini-batch learning on any narwhals-supported eager dataframe (pandas, polars, pyarrow, ...)alphasmoothing parametercheck_estimatorTests
tests/naive_bayes/test_naive_bayes.py: learn_many vs learn_one equivalence, sparse input, sklearn equivalence, not-fitted behaviorResults match
sklearn.naive_bayes.CategoricalNBon the same data.Release note
## naive_bayes