Linear.LinearRegression

constructor()

This class implements ordinary least squares linear regression. It estimates coefficients for a linear model by minimizing the squared error between predicted and actual values.

Methods

• fit(X: number[][], Y: number[]): void
• predict(X: number[][]): number[]

fit

• X - Feature matrix of shape [nSamples, nFeatures].
• Y - Target values of length nSamples.

predict

• X - Feature matrix for which to compute predictions.

Example

const lr = new LinearRegression();
lr.fit([[0], [1]], [1, 3]);
const pred = lr.predict([[2]]); // about 5

Practical guide: LinearRegression in JavaScript and TypeScript

LinearRegression models continuous targets with an interpretable linear relationship between features and outputs.

When to use LinearRegression

• You need a transparent baseline for numeric prediction tasks.
• Feature-target relationships are approximately linear after transformation.
• You want fast training and low-latency inference in JavaScript.

Implementation workflow

1. Prepare numeric features and split into train/validation sets.
2. Fit the model and inspect residual patterns for systematic errors.
3. Iterate on feature engineering when residuals show non-linear structure.

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 LinearRegression