Module 4: Model Interpretation
Module Overview
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
- Model Interpretability
- Visualize and interpret PDP plots
- Explain individual predictions with shapley value plots
Guided Project
Open DS_234_guided_project_notes.ipynb in the GitHub repository below to follow along with the guided project:
Guided Project Video - Part One
Guided Project Video - Part Two
Module Assignment
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.