Lasso Regression: A Comprehensive Guide to Feature Selection and Regularization

In the world of machine learning and statistical modeling, Lasso regression (short for “Least Absolute Shrinkage and Selection Operator”) is a widely employed technique that provides valuable insights into feature selection and regularization. This blog post aims to serve as a comprehensive guide to understanding Lasso regression, covering its fundamental concepts, mathematical formulation, and implementation in Python.

Understanding Linear Regression

Linear regression is a fundamental technique in statistical modeling used to establish a linear relationship between a dependent variable and one or more independent variables. It serves as the foundation for more advanced regression techniques like Lasso regression.

1. Basic Equation: The basic equation of linear regression can be represented as follows:

y = β₀ + β₁x₁ + β₂x₂ + … + βₚxₚ + ɛ

where:

• y is the dependent variable (also known as the target or response variable).
• β₀ represents the intercept term.
• β₁, β₂, ..., βₚ are the regression coefficients associated with the independent variables x₁, x₂, ..., xₚ.
• ɛ is the error term, which…