Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
Author: Aditya Bhattacharya
Publisher Finelybook 出版社:‎Packt Publishing (July 29, 2022)
Language 语言:‎English
pages 页数:‎306 pages
ISBN-10 书号:‎1803246154
ISBN-13 书号:‎9781803246154

Book Description
Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems

Key Features

Explore various explainability methods for designing robust and scalable explainable ML systems
Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems
Design user-centric explainable ML systems using guidelines provided for industrial applications


Book Description

Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases.

Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin Author: gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users.

Author: the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered.

What you will learn

Explore various explanation methods and their evaluation criteria
Learn model explanation methods for structured and unstructured data
Apply data-centric XAI for practical problem-solving
Hands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and others
Discover industrial best practices for explainable ML systems
Use user-centric XAI to bring AI closer to non-technical end users
Address open challenges in XAI using the recommended guidelines

Who this book is for

This book is designed for scientists, researchers, engineers, architects, and managers who are actively engaged in the field of Machine Learning and related areas. In general, anyone who is interested in problem-solving using AI would be benefited from this book. The readers are recommended to have a foundational knowledge of Python, Machine Learning, Deep Learning, and Data Science. This book is ideal for readers who are working in the following roles:

Data and AI Scientists
AI/ML Engineers
AI/ML Product Managers
AI Product Owners
AI/ML Researchers
User experience and HCI Researchers

Table of Contents
1.Foundational Concepts of Explainability Techniques
2.Model Explainability Methods
3.Data-Centric Approaches
4.LIME for Model Interpretability
5.Practical Exposure to using LIME in ML
6.Model Interpretability Using SHAP
7.Practical Exposure to using SHAP in ML
8.Human-Friendly Explanations with TCAV
9.Other Popular XAl Frameworks
1o.XAl Industry Best Practices
n.End user-Centered Artificial Intelligence

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