Lasso Regression, Ridge Regression, L1 Regularization, L2 Regularization.
between the input features and the target variable. The model is represented by the equation: y = β0​ + β1​x1 ​+ β2​x2​ +…+ βn​xn​ + ϵ Here, y is the target variable x1​,x2​,…,xn​ are the input features, β0​,β1​,…,βn​ are the coefficients, and ϵ is the error term. The Need for Regularization When