This is an implementation of the logistic regression. When there are more than 2 classes, the method used is the One VS All.
$ npm i ml-logistic-regression
const { Matrix } = require('ml-matrix');
// Our training set (X,Y).
const X = new Matrix([[0, -1], [1, 0], [1, 1], [1, -1], [2, 0], [2, 1], [2, -1], [3, 2], [0, 4], [1, 3], [1, 4], [1, 5], [2, 3], [2, 4], [2, 5], [3, 4], [1, 10], [1, 12], [2, 10], [2, 11], [2, 14], [3, 11]]);
const Y = Matrix.columnVector([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2]);
// The test set (Xtest, Ytest).
const Xtest = new Matrix([
[0, -2],
[1, 0.5],
[1.5, -1],
[1, 2.5],
[2, 3.5],
[1.5, 4],
[1, 10.5],
[2.5, 10.5],
[2, 11.5],
]);
const Ytest = Matrix.columnVector([0, 0, 0, 1, 1, 1, 2, 2, 2]);
// We will train our model.
const logreg = new LogisticRegression({ numSteps: 1000, learningRate: 5e-3 });
logreg.train(X, Y);
// We try to predict the test set.
const finalResults = logreg.predict(Xtest);
// Now, you can compare finalResults with the Ytest, which is what you wanted to have.