deepbox/ml
Linear Models
Linear, regularized, robust, kernel, probabilistic, and SGD-based models for regression and classification.
Linear estimators
BayesianRidge
Bayesian Ridge Regression with automatic regularization.
ElasticNet
Elastic Net Regression (L1 + L2 Regularized Linear Regression).
HuberRegressor
Linear regression with Huber loss for robustness to outliers.
IsotonicRegression
Isotonic Regression.
KernelRidge
Kernel Ridge Regression.
Lasso
Lasso Regression (L1 Regularized Linear Regression).
LinearRegression
Ordinary Least Squares Linear Regression.
LogisticRegression
Logistic Regression (Binary and Multiclass Classification).
QuantileRegressor
QuantileRegressor is part of the deepbox/ml public API.
RANSACRegressor
RANSACRegressor is part of the deepbox/ml public API.
Ridge
Ridge Regression (L2 Regularized Linear Regression).
SGDClassifier
SGD Classifier — Linear classifiers with SGD training.
SGDRegressor
SGD Regressor — Linear regressor with SGD training.
ml-linear.ts
import { BayesianRidge, ElasticNet, LinearRegression, LogisticRegression, Ridge,} from "deepbox/ml";import { tensor } from "deepbox/ndarray";const X = tensor([[1], [2], [3], [4]]);const y = tensor([2, 4, 6, 8]);console.log(new LinearRegression().fit(X, y).predict(tensor([[5]])).toString());console.log(new Ridge({ alpha: 1 }).fit(X, y).predict(tensor([[5]])).toString());console.log(new ElasticNet({ alpha: 0.1 }).fit(X, y).predict(tensor([[5]])).toString());console.log(new BayesianRidge().fit(X, y).predict(tensor([[5]])).toString());console.log(new LogisticRegression().fit(tensor([[0], [1], [2], [3]]), tensor([0, 0, 1, 1])).predict(tensor([[1.5]])).toString());