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Lasso Regression

This module contains an implementation of Lasso Regression, a linear regression technique with L1 regularization.

Overview

Lasso Regression is a regression algorithm that adds a penalty term based on the absolute values of the coefficients. This penalty term helps in feature selection by driving some of the coefficients to exactly zero, effectively ignoring certain features.

Usage

To use Lasso Regression, follow these steps:

  1. Import the LassoRegression class.
  2. Create an instance of the class, specifying parameters like learning rate, lambda (regularization strength), and the number of iterations.
  3. Fit the model to your training data using the fit method.
  4. Make predictions using the predict method.

Example:

from LassoRegression import LassoRegression

lasso_model = LassoRegression(learning_rate=0.01, lambda_param=0.1, n_iterations=1000)
lasso_model.fit(X_train, y_train)
predictions = lasso_model.predict(X_test)

Parameters

  • learning_rate: The step size for gradient descent.
  • lambda_param: Regularization strength (L1 penalty).
  • n_iterations: The number of iterations for gradient descent.

Installation

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

pip install numpy

Coded By

Avdhesh Varshney

Happy Coding 👦