In this class, I cover the processes involved in measuring feature importance. I cover measuring importance with SHAP values and tree-based model importance measurement with Gradient Boosted Trees and Random Forest. I then cover how to perform feature selection for machine learning models. I demo all concepts with Python code.

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Data Available Here and Code Available Here

Suggested Reading:

• Python shap package documentation
• A Unique Method for Machine Learning Interpretability: Game Theory & Shapley Values!
• Explain Your Model with the SHAP Values
• Chapter 5 and Chapter 6 from Interpretable Machine Learning with Python by Serg Masis