bro understood the assignment

I hear you. All right, what about you explain the difference between L1 and L2 regularization and when would you choose one over the other? When would you choose each? Answer: L1 regularization (Lasso) adds the absolute value of the coefficients as a penalty term to the loss function. L2 regularization (Ridge) adds the squared value of the coefficients as a penalty term to the loss function. Key differences: L1 tends to produce sparse models by driving some coefficients exactly to zero, which effectively performing feature selection. L2 tends to shrink coefficients evenly but rarely makes them exactly zero, which helps with multicollinearity and stabilizes the model. When to choose L1: When you want a simpler, more interpretable model with fewer features. When you suspect many features are irrelevant and want automatic feature selection. When to choose L2: When you want to keep all features but reduce overfitting by shrinking coefficients. That's simple. So I would choose L1 because it tends to produce sparse models.