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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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  • Description
  • Acknowledgments

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  1. Datasets

Medical Cost Personal Dataset

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Last updated 4 years ago

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Description

This dataset contains information about the medical charges incurred by an individual in the United States along with their personal details such as age, gender, body mass index(BMI), number of children, smoker/non-smoker, residential area. The charges are an important point of estimation for any insurance company.

Data Set Characteristics: Regression

Number of Instances: 1338

Features

  • Age

  • Sex

  • BMI

  • Children

  • Smoker

  • Region

  • Charges

Acknowledgments

The data set is available .

here