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
- Decision Trees
Explainability Techniques for Classical ML
Datasets
- Medical Cost Personal Dataset
- Telecom Churn Dataset
- Sales Opportunity Size Dataset
- Pima Indians Diabetes Dataset
Implementation of these techniques on different models
Future scope
Contributors
Citing this Book

Powered by GitBook

On this page

Contributors

PreviousFuture scope NextCiting this Book

Last updated 3 years ago

Was this helpful?

CtrlK

Was this helpful?

Book had contributions from other interested people who have worked on parts of the book and helped in developing some of the google colab.

Pramod Pai

Prajwal Mylar

Varun Raj Vavilal

Srushti Sanjay Dhamangaonkar