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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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  • Author:
  • Abhishek Gargha Maheshwarappa

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Explainable AI

Making a AI System TrustWorthy

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Last updated 3 years ago

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A practical guide for using different Explainable AI techniques and Tools

Author:

Abhishek Gargha Maheshwarappa

" Hey, I like working on Data Science problems that can add value to the business. My interest lies in DeepLearning Reinforcement Learning and Explainable AI. Open to connect anytime if you want to talk."

Kartik Kumar

" likes to solve business problems using data science and cloud technologies. Currently, works as a Data engineer to design end to end solutions for such problems on GCP "

Mentor:

Open to connect at - Read more about me -

Open to connect at -

Nicholas Brown (Assistant Teaching Professor, Multidisciplinary Graduate Engineering Programs, Northeastern University)

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