What are some common mistakes in feature selection for machine learning?

What are some common mistakes in feature selection for machine learning?

Some common mistakes include ignoring feature correlation, not considering feature importance, and overfitting.

Can you explain why ignoring feature correlation is a mistake?

Ignoring feature correlation can lead to redundancy, where multiple features provide the same information, which can skew model performance.

What about feature importance? How should I assess it?

You can assess feature importance using techniques like permutation importance, tree-based methods, or regularization methods.

Are there any tools that can help with feature selection?

Yes, tools like Scikit-learn, Boruta, and Lasso regression can assist in effective feature selection.