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

✍ Scribed by Aditya Bhattacharya

Publisher: Packt Publishing
Year: 2022
Tongue: English
Leaves: 306
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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 by 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.

By 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

Foundational Concepts of Explainability Techniques
Model Explainability Methods
Data-Centric Approaches
LIME for Model Interpretability
Practical Exposure to Using LIME in ML
Model Interpretability Using SHAP
Practical Exposure to Using SHAP in ML
Human-Friendly Explanations with TCAV
Other Popular XAI Frameworks
XAI Industry Best Practices
End User-Centered Artificial Intelligence

📜 SIMILAR VOLUMES

Applied Machine Learning Explainability

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

✍ Aditya Bhattacharya 📂 Library 📅 2022 🏛 Packt Publishing 🌐 English

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 <ul><li>Explore various explainability me

Human and Machine Learning: Visible, Exp

📁 Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent

✍ Jianlong Zhou, Fang Chen 📂 Library 📅 2018 🏛 Springer 🌐 English

With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be int

Human and Machine Learning : Visible, Ex

📁 Human and Machine Learning : Visible, Explainable, Trustworthy and Transparent

✍ Jianlong Zhou; Fang Chen 📂 Library 📅 2018 🏛 Springer International Publishing 🌐 English

Explainable Machine Learning for Multime

📁 Explainable Machine Learning for Multimedia Based Healthcare Applications

✍ M. Shamim Hossain (editor), Utku Kose (editor), Deepak Gupta (editor) 📂 Library 📅 2023 🏛 Springer 🌐 English

This book covers the latest research studies regarding Explainable Machine Learning used in multimedia-based healthcare applications. In this context, the content includes not only introductions for applied research efforts but also theoretical touches and discussions targeting open problems a

Explainable Machine Learning Models and

📁 Explainable Machine Learning Models and Architectures

✍ Suman Lata Tripathi, Mufti Mahmud 📂 Library 📅 2023 🏛 Scrivener Publishing 🌐 English

Machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. L

Practical Explainable AI Using Python: A

📁 Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks

✍ Pradeepta Mishra 📂 Library 📅 2022 🏛 Apress 🌐 English

Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as