Skip to main content

​
Overview

Ridge implements linear regression with L2 regularization. The regularization term penalizes large coefficients, which helps prevent overfitting and improves model generalization. Ridge uses a closed-form solution for efficient fitting.

​
Constructor

import { Ridge } from "bun-scikit";

const model = new Ridge(options);

​
Parameters

​
options
RidgeOptions
default:"{}"
Configuration options for the model

​
Methods

​
fit

Fit the ridge regression model using training data. 
fit(X: Matrix, y: Vector, sampleWeight?: Vector): this
Parameters:
  • X: Training data matrix of shape [nSamples, nFeatures]
  • y: Target values vector of length nSamples
  • sampleWeight: Optional sample weights (not currently used)
Returns: The fitted model instance Example: 
const X = [[0], [1], [2], [3], [4], [5]];
const y = [0, 1, 2, 3, 4, 5];

const ridge = new Ridge({ alpha: 1 });
ridge.fit(X, y);

​
predict

Predict using the ridge regression model. 
predict(X: Matrix): Vector
Parameters:
  • X: Samples matrix of shape [nSamples, nFeatures]
Returns: Predicted values vector of length nSamples Example: 
const predictions = ridge.predict([[2.5]]);
console.log(predictions[0]); // Predicted value

​
score

Return the coefficient of determination (R² score) of the prediction. 
score(X: Matrix, y: Vector): number
Parameters:
  • X: Test samples matrix
  • y: True target values
Returns: R² score (coefficient of determination)

​
Attributes

After fitting, the following attributes are available:
​
coef_
Vector
Estimated coefficients for the ridge regression problem
​
intercept_
number
Independent term in the linear model

​
Complete Example

import { Ridge } from "bun-scikit";

// Training data
const X = [[0], [1], [2], [3], [4], [5]];
const y = [0, 1, 2, 3, 4, 5];

// Create and fit the model
const ridge = new Ridge({ alpha: 1 });
ridge.fit(X, y);

// Evaluate the model
const r2 = ridge.score(X, y);
console.log("R² score:", r2); // >0.98

// Make predictions
const predictions = ridge.predict(X);
console.log("Predictions:", predictions);

// Check coefficients
console.log("Coefficients:", ridge.coef_);
console.log("Intercept:", ridge.intercept_);

​
Comparison Example

import { Ridge, LinearRegression } from "bun-scikit";

// Compare Ridge with different alpha values
const X = [[0], [1], [2], [3], [4], [5]];
const y = [0, 1.1, 1.9, 3.1, 4.1, 4.9];

const models = [
  new LinearRegression(),
  new Ridge({ alpha: 0.1 }),
  new Ridge({ alpha: 1.0 }),
  new Ridge({ alpha: 10.0 }),
];

models.forEach(model => {
  model.fit(X, y);
  const r2 = model.score(X, y);
  console.log(`${model.constructor.name} R²: ${r2.toFixed(4)}`);
});

​
Notes

  • Ridge regression uses a closed-form solution, making it very fast to fit.
  • The alpha parameter controls the trade-off between fitting the training data and keeping the coefficients small.
  • Ridge never sets coefficients exactly to zero (unlike Lasso). For feature selection, consider Lasso or ElasticNet.
  • For automatic selection of the best alpha value, see RidgeCV.

Build docs developers (and LLMs) love

Get started for freeTalk to us