Lasso Regression, Ridge Regression, L1 Regularization, L2 Regularization.
square of sum of absolute value of weights. lambda is the tuning parameter which decides how much we want to penalize the model J(theta) is the cost function Applications and Use Cases Lasso Regression: Feature Selection: Lasso is effective in selecting a subset of relevant features, promoting