Feature Importance and SHAP
What the model relied on most.
Which Features Mattered?
After a model trains, you want to know which inputs drove its predictions. That ranking is called feature importance. 🔍
Built-In Tree Importance
Tree models expose a quick feature_importances_ array. Higher values mean the feature was used more often to split the data.
importances = model.feature_importances_All lessons in this course
- Feature Importance and SHAP
- Partial Dependence Intuition
- Charts That Persuade Stakeholders
- From Notebook to Dashboard