Ridge Regression

This module contains an implementation of Ridge Regression, a linear regression variant that includes regularization to prevent overfitting.

Overview

Ridge Regression is a linear regression technique with an added regularization term to handle multicollinearity and prevent the model from becoming too complex.

Usage

To use Ridge Regression, follow these steps:

1. Import the RidgeRegression class.
2. Create an instance of the class, specifying the regularization parameter (alpha).
3. Fit the model to your training data using the fit method.
4. Make predictions using the predict method.

Example:

from RidgeRegression import RidgeRegression

ridge_model = RidgeRegression(alpha=0.1)
ridge_model.fit(X_train, y_train)
predictions = ridge_model.predict(X_test)

Parameters

• alpha: Regularization strength. A higher alpha increases the penalty for large coefficients.

Installation

To use this module, make sure you have the required dependencies installed:

pip install numpy

Coded By

Avdhesh Varshney

Happy Coding 👦