Bayes.CategoricalNB

Naive Bayes classifier for features with a finite number of discrete categories. It counts how often each feature value appears in each class and uses additive smoothing to estimate the conditional probabilities.

interface CategoricalNBProps {
    alpha?: number;
    forceAlpha?: boolean;
    fitPrior?: boolean;
    classPrior?: number[] | null;
    minCategories?: number | number[] | null;
}
constructor(props: CategoricalNBProps = {})

Parameters

• alpha — Smoothing parameter used when computing category probabilities. Larger values make the model less sensitive to missing observations.
• forceAlpha — If true, ensures that alpha is strictly positive even when a small value is provided.
• fitPrior — Whether to learn class prior probabilities from data. When false, class priors are assumed to be uniform.
• classPrior — Optional array of prior probabilities for each class. Overrides the data-derived priors when provided.
• minCategories — Minimum number of categories assumed for each feature. Can be a single number or an array specifying the value per feature.

Example

const clf = new CategoricalNB();
clf.fit(trainX, trainY);
const result = clf.predict(testX);