SVM.SVC

Support Vector Classification. When the kernel is set to rbf it applies the kernel trick to transform input features before training a linear classifier.

interface SVCProps {
    C?: number;
    maxIter?: number;
    learningRate?: number;
    kernel?: 'linear' | 'rbf';
    gamma?: number;
}
constructor(props: SVCProps = {})

Parameters

• C (number, default 1): regularization strength
• maxIter (number, default 100): maximum iterations for training
• learningRate (number, default 0.01): step size for the optimizer
• kernel ('linear' | 'rbf', default 'rbf'): kernel type
• gamma (number, default 1): RBF kernel coefficient

Example

const svc = new SVC({ kernel: 'linear' });
svc.fit(X, y);
const result = svc.predict(T);

Practical guide: SVC in JavaScript and TypeScript

SVC trains kernelized support vector classifiers for non-linear decision boundaries.

When to use SVC

• Linear models underfit complex class separation patterns.
• You can afford kernel-based training for improved boundary flexibility.
• You need strong classification performance on medium-sized datasets.

Implementation workflow

1. Scale features and pick a kernel (RBF is a common starting point).
2. Fit with baseline hyperparameters and assess validation metrics.
3. Tune C, kernel settings, and class weights for task-specific goals.

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

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• @kanaries/ml SVC