Polynomial Regression

Algorithm overview

Polynomial regression expands each numeric feature into powers of that feature, then fits a linear model on the expanded matrix. It is useful when a linear baseline is too simple but the target still follows a smooth curve.

Use it for small numeric regression problems where the degree of curvature is known or easy to tune.

JavaScript implementation

@kanaries/ml provides Linear.PolynomialRegression as a JavaScript estimator with fit and predict. It runs in browser or Node.js and keeps polynomial feature expansion inside the model.

Quick start example

import { Linear } from '@kanaries/ml';

const X = [[0], [1], [2], [3]];
const y = [1, 2, 5, 10];

const model = new Linear.PolynomialRegression({ degree: 2 });
model.fit(X, y);
const pred = model.predict([[4]]);

Detailed API reference

new Linear.PolynomialRegression(props?: { degree?: number })

Options:

• degree?: number, default 2. Must be an integer greater than or equal to 1.

Methods:

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

The implementation expands every input feature to powers from 1 through degree, fits ordinary least squares, and stores an intercept plus coefficients internally.