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);

Practical guide: CategoricalNB in JavaScript and TypeScript

CategoricalNB is designed for discrete categorical features encoded as integer category IDs.

When to use CategoricalNB

• Inputs are naturally categorical and not meaningful on a numeric distance scale.
• You need fast classification with probabilistic interpretation.
• You want a robust baseline for tabular categoricals in pure JavaScript.

Implementation workflow

1. Encode every categorical column to stable integer category indices.
2. Fit on labeled rows and validate per-class calibration quality.
3. Monitor unseen-category behavior and keep category mapping versioned.

JavaScript deployment notes

• Prefer feature scaling for distance-based and gradient-based algorithms to improve stability.
• In browser apps, run heavy training in Web Workers to keep UI interactions smooth.
• Keep a simple baseline from the same module as a fallback model for comparison.

Search intents this page targets

• CategoricalNB JavaScript
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• @kanaries/ml CategoricalNB