All Courses

Model Interpretability with SHAP and LIME

Chapter 1: Foundations of Model Interpretability

Why Explain Model Predictions?

Interpretability vs. Explainability

Taxonomy of Interpretability Methods

Scope of Explanations: Global vs. Local

Challenges in Model Interpretation

Quiz for Chapter 1

Chapter 2: Local Interpretable Model-agnostic Explanations (LIME)

Intuition Behind LIME

How LIME Works: Perturbation and Surrogate Models

Applying LIME to Tabular Data

Applying LIME to Text Data

Interpreting LIME Explanations

LIME Implementation with Python

Limitations and Considerations for LIME

Hands-on Practical: Generating LIME Explanations

Quiz for Chapter 2

Chapter 3: SHapley Additive exPlanations (SHAP)

Introduction to Shapley Values

SHAP Values: Connecting Shapley to Model Features

Properties of SHAP Values

KernelSHAP: A Model-Agnostic Approach

TreeSHAP: Optimized for Tree-Based Models

Interpreting SHAP Plots: Force Plots

Interpreting SHAP Plots: Summary and Dependence Plots

SHAP Implementation with Python

Hands-on Practical: Calculating SHAP Values

Quiz for Chapter 3

Chapter 4: Comparing and Applying Interpretability Techniques

LIME vs. SHAP: Differences

LIME vs. SHAP: Strengths and Weaknesses

Choosing Between LIME and SHAP

Interpreting Explanations for Regression Models

Interpreting Explanations for Classification Models

Integrating Interpretability into the ML Workflow

Common Gotchas and Considerations

Practice: Comparing LIME and SHAP Outputs

Quiz for Chapter 4

Quiz
Beta

Chapter: Local Interpretable Model-agnostic Explanations (LIME)

Test your understanding and practice the concepts from this chapter

Quiz Instructions

This quiz contains 14 questions to help you practice.
You need to score at least 70% to pass.
Attempts: Unlimited.
Your highest score will be kept.
Please attempt this quiz without assistance; however, feel free to refer to the chapter notes or use a code interpreter if needed.
This assessment is for your learning and understanding, It is not formally accredited (yet).

Question Format

The questions are designed to be engaging, focusing on understanding, application, and interpretation rather than rote memorization. Expect scenario-based problems that test your ability to apply what you've learned.

Attempts

Best scores and quiz attempts will appear.

© 2025 ApX Machine Learning