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
Datasets
Implementation of these techniques on different models
Future scope
Contributors
Citing this Book

Powered by GitBook

On this page

Was this helpful?

How to use this book?

Note

Restructuring of the book is in progress, updates will follow in the last week of December

PreviousPreface NextContents

Last updated 3 years ago