SVM.LinearSVR

Linear support vector regression trained with gradient descent on the epsilon-insensitive loss.

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

Parameters

• epsilon (number, default 0): width of the insensitive tube around the regression line
• C (number, default 1): regularization strength
• maxIter (number, default 100): maximum training iterations
• learningRate (number, default 0.01): optimizer step size

Example

const reg = new LinearSVR();
reg.fit(X, y);
const preds = reg.predict(T);

Practical guide: LinearSVR in JavaScript and TypeScript

LinearSVR performs margin-based linear regression with robustness to moderate outliers in target values.

When to use LinearSVR

• You need regression in high-dimensional spaces with linear assumptions.
• Outlier sensitivity from ordinary least squares is causing instability.
• You want a fast margin-based baseline in Node.js services.

Implementation workflow

1. Prepare standardized numeric features and choose epsilon margin.
2. Fit LinearSVR and measure MAE/RMSE on holdout data.
3. Tune C and epsilon for error tolerance versus fit quality.

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 LinearSVR