Lasso Regression

Algorithm overview

Lasso regression uses L1 regularization, which can shrink some coefficients to zero. It is useful when you want a linear model that can perform simple feature selection while controlling overfitting.

JavaScript implementation

@kanaries/ml implements Linear.LassoRegression with coordinate-descent style optimization and a JavaScript API for browser or Node.js applications.

Quick start example

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

const X = [[0, 1], [1, 1], [2, 0], [3, 0]];
const y = [1, 2, 3, 4];

const model = new Linear.LassoRegression({ alpha: 0.1, maxIter: 1000, tol: 1e-6 });
model.fit(X, y);
const pred = model.predict([[4, 0]]);

Detailed API reference

new Linear.LassoRegression(props?: {
  alpha?: number;
  fitIntercept?: boolean;
  maxIter?: number;
  tol?: number;
})

Options:

• alpha?: number, default 1. L1 penalty strength.
• fitIntercept?: boolean, default true.
• maxIter?: number, default 1000.
• tol?: number, default 1e-6.

Methods:

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

sklearn-style alias: Lasso

Linear.Lasso extends Linear.LassoRegression with identical options and methods. Use it when you prefer the scikit-learn class name:

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

const model = new Linear.Lasso({ alpha: 0.1, fitIntercept: true });
model.fit([[0, 1], [1, 1], [2, 0]], [1, 2, 3]);
const pred = model.predict([[3, 0]]);