bayes-classifier

A lightweight collection of Bayes classifiers for tabular data.

Included classifiers:

• MultivariateGaussianBayesClassifier
• GaussianNaiveBayesClassifier
• MultinomialBayesClassifier
• BernoulliBayesClassifier
• CategoricalBayesClassifier
• PoissonBayesClassifier

BayesClassifier is provided as an alias of MultivariateGaussianBayesClassifier.

Install

pip install bayes-classifier

Quickstart

import numpy as np
from bayes_classifier import GaussianNaiveBayesClassifier

X = np.array([
    [1.0, 2.0],
    [1.2, 1.8],
    [3.0, 3.2],
    [2.8, 3.1],
])
y = np.array([0, 0, 1, 1])

clf = GaussianNaiveBayesClassifier().fit(X, y)
preds = clf.predict(X)

Classifier Guide

Use the classifier that matches your feature distribution:

• MultivariateGaussianBayesClassifier: continuous features with full covariance.
• GaussianNaiveBayesClassifier: continuous features with conditional independence assumption.
• MultinomialBayesClassifier: non-negative count-like features.
• BernoulliBayesClassifier: binary/presence features (inputs are thresholded to 0/1).
• CategoricalBayesClassifier: discrete category-valued features.
• PoissonBayesClassifier: non-negative count features modeled with Poisson rates.

API

Each classifier follows the same simple interface:

model = SomeBayesClassifier(...)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)