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Explainable-AI
  • Explainable AI
  • Preface
  • How to use this book?
  • Contents
  • What is Explainable AI?
  • Why do we need Explainablity?
  • Explainable systems and Black box systems
  • Types of Explainability Techniques
  • Explainable Models
    • Linear Regression
      • Assumptions
      • Model
      • Statistical Interpretation
    • Decision Trees
      • How Do They Work?
      • Creating the model
      • Interpretation
  • Explainability Techniques for Classical ML
    • SHAP (SHapley Additive exPlanations)
    • Surrogate model
    • LIME (Local Interpretable Model-Agnostic Explanations)
    • PDP (Partial Dependence Plot)
    • ICE (Individual Conditional Expectation Plots)
    • ALE (Accumulated Local Effects Plot)
  • Datasets
    • Medical Cost Personal Dataset
    • Telecom Churn Dataset
    • Sales Opportunity Size Dataset
    • Pima Indians Diabetes Dataset
  • Implementation of these techniques on different models
    • Logistic Regression - SHAP
    • Random Forest - LIME
    • GBM - PDP
    • GBM - ICE
    • Deep Learning - Surrogate
  • Future scope
  • Contributors
  • Citing this Book
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Last updated 3 years ago

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