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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

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

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

Chapter 4: Comparing and Applying Interpretability Techniques

LIME vs. SHAP: Conceptual 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

Model Interpretability with SHAP and LIME

Prerequisites: Familiarity with basic machine learning concepts (classification, regression) and Python programming.

Level: Intermediate

What You'll Learn

Interpretability Concepts
Distinguish between interpretability and explainability, and understand why they are necessary for machine learning models.
LIME Implementation
Apply the LIME algorithm to explain individual predictions of various machine learning models.
SHAP Implementation
Utilize the SHAP framework and its different explainers to generate local and global explanations for model behavior.
Explanation Evaluation
Understand how to interpret and compare the outputs generated by LIME and SHAP.
Practical Application
Integrate interpretability techniques into a standard machine learning workflow using Python libraries.

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