When should I use Lasso or Ridge regression?

Can you explain when to use Lasso or Ridge regression?

Sure! Lasso regression is great for feature selection, while Ridge regression is better for handling multicollinearity.

What are the main differences between them?

Lasso adds a penalty equal to the absolute value of the coefficients, which can shrink some to zero. Ridge adds a penalty equal to the square of the coefficients, which keeps all features but reduces their impact.

In what scenarios would I prefer Lasso over Ridge?

You'd prefer Lasso when you have a lot of features and suspect that only a few are important, as it can help simplify your model.

And when should I choose Ridge?

Choose Ridge when you have many correlated features, as it will distribute the coefficient values more evenly among them.