SVM.LinearSVC

Linear support vector classifier optimized with gradient descent on hinge loss. It trains one-versus-rest linear models to separate classes.

interface LinearSVCProps {
    C?: number;
    maxIter?: number;
    learningRate?: number;
}
constructor(props: LinearSVCProps = {})

Parameters

• C (number, default 1): regularization strength
• maxIter (number, default 100): maximum training iterations
• learningRate (number, default 0.01): step size for gradient descent

Example

const svc = new LinearSVC();
svc.fit(X, y);
const result = svc.predict(T);

Practical guide: LinearSVC in JavaScript and TypeScript

LinearSVC trains linear support vector classifiers optimized for high-dimensional and sparse feature spaces.

When to use LinearSVC

• You have many features (for example text vectors) and need fast linear classification.
• Margin-based classification outperforms logistic baselines in your tests.
• You need robust performance with controlled model complexity.

Implementation workflow

1. Scale/normalize features and choose regularization strength.
2. Fit the classifier and evaluate margin-driven classification metrics.
3. Tune C and class weighting to match recall/precision priorities.

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

• LinearSVC JavaScript
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• @kanaries/ml LinearSVC