Module 4: Model Interpretation
In this final module of the sprint, you'll learn techniques for interpreting machine learning models and explaining their predictions. Model interpretability is crucial for building stakeholder trust, ensuring ethical decision-making, debugging models, and gaining insights into your data that you can communicate effectively.
Learning Objectives
1. Model Interpretability
Learn the importance of model interpretability and techniques for making models more transparent and understandable.
- Understanding the trade-off between model complexity and interpretability
- Learning different types of model interpretability approaches (intrinsic vs post-hoc)
- Identifying situations where interpretability is critical
- Comparing global vs local interpretation methods
- Understanding the limitations of black-box models
- Implementing model-agnostic interpretation techniques
2. Visualize and interpret PDP plots
Learn how to create and interpret Partial Dependence Plots (PDP) to understand how features affect model predictions.
- Understanding what partial dependence plots reveal about feature effects
- Creating PDPs using interpretation libraries like PDPBox
- Interpreting single-feature and two-feature interaction PDPs
- Using PDPs to guide feature engineering decisions
- Identifying non-linear relationships between features and predictions
- Understanding the limitations of PDPs
3. Explain individual predictions with shapley value plots
Learn how to use SHAP (SHapley Additive exPlanations) values to explain individual predictions and understand feature contributions.
- Understanding the mathematical principles behind Shapley values
- Implementing SHAP for different model types
- Creating and interpreting force plots for individual predictions
- Using SHAP to identify feature importance globally and locally
- Comparing SHAP with other feature importance methods
- Communicating model decisions effectively using SHAP visualizations
Guided Project
Model Interpretation Guided Project
Part Two Video
In this guided project, you'll work through a complete workflow for model interpretation, using techniques like partial dependence plots and SHAP values to understand how your model makes predictions and which features have the greatest impact.
Module Assignment
Model Interpretation for Your Portfolio Project
For this final assignment, you'll apply model interpretation techniques to your portfolio project to gain insights and effectively communicate your model's behavior.
Note: There is no video for this assignment as you will be working with your own dataset and defining your own machine learning problem.
Assignment Notebook Name: LS_DS_234_assignment.ipynb
Tasks:
- Continue to iterate on your project: data cleaning, exploratory visualization, feature engineering, modeling.
- Make at least 1 partial dependence plot to explain your model.
- Make at least 1 Shapley force plot to explain an individual prediction.