How do I choose between Lasso and Ridge regression?

I'm trying to decide between Lasso and Ridge regression for my model. Can you help?

Sure! Both Lasso and Ridge are regularization techniques, but they have different strengths. What are your main goals?

I want to reduce overfitting and improve model interpretability.

In that case, Lasso might be a better choice since it can shrink some coefficients to zero, effectively selecting features.

What if I have a lot of correlated features?

Ridge regression is generally better for handling multicollinearity, as it tends to distribute the coefficient values more evenly.

So, should I try both and compare their performance?

Yes, that's a great approach! You can use cross-validation to evaluate which model performs better on your data.