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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

Tradeoff and Limitations

Practice: Comparing LIME and SHAP Outputs

Quiz for Chapter 4

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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

Tradeoff and Limitations

Practice: Comparing LIME and SHAP Outputs

Quiz for Chapter 4