ml-regression-lasso v0.1.2

ml-regression-lasso

Lasso (least absolute shrinkage and selection operator) Regression

Installation

yarn add ml-regression-lasso

API

new LassoRegression(x, y, options)

Arguments

• x: Matrix containing the inputs
• y: Matrix containing the outputs

Options

• lambda: Constant that multiplies the L1 term, controlling regularization strength. Lambda must be a non-negative float i.e. in [0, inf)(default:0)
• tolerance: The tolerance for the optimization: if the updates are smaller than tol, the coordinate descent algorithm is considered converged. (default: 0.00001)
• maxIter: The maximum number of iterations.(default: 200)

Usage

import LassoRegression from "ml-regression-lasso";
const  x = [
	[1,32,120],
	[2,34,150],
	[3,94,40],
	[4,54,2],
];
const  y = [
	[757,378.5],
	[944,472],
	[349,174.5],
	[86,43],
];
const  lasso = new  LassoRegression(x, y, { lambda:  0.1 });
console.log(lasso.weights);
/*
[
  [ 0, 0 ],
  [ 0, 0 ],
  [ 5.078686699741143, 2.5393433498705713 ],
  [ 137.86243742019087, 68.93121871009544 ]
]
*/
console.log(lasso.predict(x));
/*
[
  [ 747.304841389128, 373.652420694564 ],
  [ 899.6654423813623, 449.8327211906811 ],
  [ 341.00990540983656, 170.50495270491828 ],
  [ 148.01981081967315, 74.00990540983658 ]
]
*/

Acknowledgments

This work was inspired by mljs. Lasso regression was not in the included libraries, and I wasn't able to find one readily available that matched the results of scikitLearn.

The algorithm is based on this article.

License

MIT