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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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Contents

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Last updated 3 years ago

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What is Explainable AI?
Why do we need Expalainbility?
Explainable Systems and Black Box Systems
Types of Explainability Techniques
Explainable Models
Linear Regression
Introduction
Assumptions
Model
Statistical interpretation
Decision Trees
Introduction
How Do They Work?
Creating the model
Interpretation
Model Agonistic Methods
SHapley Additive exPlanations
Surrogate model
Local Interpretable Model-Agnostic Explanations and K-LIME
Partial Dependence Plot
Individual Conditional Plots
Data sets
Medical cost personal Dataset
Telecom Churn Dataset
Sales Opportunity Size Dataset
Pima Indians Diabetes Datasets
Implementation of these techniques on different models
Logistic regression
Random forest
GBM - PDP
GBM - ICE
Deep Learning