Summary
- Feature Selection is about choosing which features to include in the model before training, aiming to improve model performance and efficiency.
- Feature Importance is about understanding the role and impact of each feature after the model has been trained, providing insights into the model’s decision-making process.
Use for interpretability of the model, but they are applied at different stages and serve different purposes.