ml-regression

ml-regression

Regression algorithms.

Installation

$ npm install ml-regression

Examples

Simple linear regression

const SLR = require("ml-regression").SLR;
let inputs = [80, 60, 10, 20, 30];
let outputs = [20, 40, 30, 50, 60];

let regression = new SLR(inputs, outputs);
regression.toString(3) === "f(x) = - 0.265 * x + 50.6";

External links

Check this cool blog post for a detailed example: https://hackernoon.com/machine-learning-with-javascript-part-1-9b97f3ed4fe5

Polynomial regression

const PolynomialRegression = require("ml-regression").PolynomialRegression;
const x = [50, 50, 50, 70, 70, 70, 80, 80, 80, 90, 90, 90, 100, 100, 100];
const y = [
  3.3, 2.8, 2.9, 2.3, 2.6, 2.1, 2.5, 2.9, 2.4, 3.0, 3.1, 2.8, 3.3, 3.5, 3.0,
];
const degree = 5; // setup the maximum degree of the polynomial
const regression = new PolynomialRegression(x, y, degree);
console.log(regression.predict(80)); // Apply the model to some x value. Prints 2.6.
console.log(regression.coefficients); // Prints the coefficients in increasing order of power (from 0 to degree).
console.log(regression.toString(3)); // Prints a human-readable version of the function.
console.log(regression.toLaTeX());

License

MIT

Changelog

6.2.0 (2024-10-17)

Features

• update dependencies (a498858)

Bug Fixes

• update robust-polynomial to avoid multiple import of regression-base (6a8853d)