Description:
This 90-minute session introduces participants to the critical topic of model interpretability, focusing on feature importance using SHAP (Shapley Additive explanations). SHAP values provide consistent, model-agnostic explanations for both regression and classification problems. Participants will learn to visualize and interpret feature contributions, enabling them to trust, debug, and communicate model decisions more effectively.
Duration: 90 mins
Course Code: BDT491
Learning Objectives:
After this course, you will be able to:
- Understand the concept of feature importance and why it matters in ML
- Explain how SHAP values quantify individual feature contributions
- Apply SHAP to both regression and classification models
- Visualize and interpret SHAP summary and dependence plots
- Use SHAP to uncover hidden patterns and model biases
- Must have some python programming experience.
- Aspiring and practicing data analysts or ML beginners who have trained basic models and now want to interpret and explain why a model is making predictions. Some familiarity with scikit-learn, regression/classification, and data manipulation with Pandas is expected.
Course Outline:
- Why Feature Importance Matters?
- Motivation: trust, transparency, accountability in ML
- Use cases in business, healthcare, finance
- Different types of feature importance (built-in, permutation, SHAP)
- Introduction to SHAP Values
- What are SHAP values? (Game theory roots simplified)
- Benefits over traditional feature importance
- Model-agnostic vs. model-specific SHAP
- Visual intuition with simple examples
- Hands-on: Visualize SHAP values
- SHAP for Regression
- Load and train a regression model (e.g., RandomForestRegressor)
- Use “shap.Explainer” to compute SHAP values
- Visualize: Summary Plot, Waterfall, etc.
- Hands-on: Interpret feature effects on housing dataset
- SHAP for Classification
- Train a classifier (e.g., Random Forest Classifier or XGBoost)
- Compute and visualize SHAP values for classification
- Class-wise explanations: understanding prediction drivers
- Feature importance and model interpretability
- Hands-on: Explore feature impacts on classification decisions
Training material provided: Yes (Digital format)
Hands-on Lab: Instructions will be provided to install Jupyter notebook and other required python libraries. Students can opt to use ‘Google Colaboratory’ if they do not want to install these tools
