Feature Engineering for Modern Machine Learning with Scikit-Learn

6.4 What Could Go Wrong?

In this chapter on feature selection with Lasso and Ridge, we’ve explored powerful techniques for optimizing model performance and reducing complexity. However, even with these tools, there are several potential pitfalls to be aware of:

6.4.1 Over-Regularization Leading to Underfitting:

• When alpha (regularization parameter) is set too high in Lasso or Ridge, it can over-penalize the model, driving too many coefficients toward zero and removing valuable features. This can lead to underfitting, where the model captures too little of the underlying data pattern.
• Solution: Use cross-validation to fine-tune the alpha parameter. Start with a broad range and gradually narrow down based on cross-validated performance.

6.4.2 Poor ...