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Key benefits

Interpret real-world data, including cardiovascular disease data and the COMPAS recidivism scores

Build your interpretability toolkit with global, local, model-agnostic, and model-specific methods

Analyze and extract insights from complex models from CNNs to BERT to time series models

Description

Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models. Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps. In addition to the step-by-step code, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. By the end of the book, you’ll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.

Who is this book for?

This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.

What you will learn

Progress from basic to advanced techniques, such as causal inference and quantifying uncertainty

Build your skillset from analyzing linear and logistic models to complex ones, such as CatBoost, CNNs, and NLP transformers

Use monotonic and interaction constraints to make fairer and safer models

Understand how to mitigate the influence of bias in datasets

Leverage sensitivity analysis factor prioritization and factor fixing for any model

Discover how to make models more reliable with adversarial robustness

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Causal Inference and Discovery in Python

$53.99

Interpretable Machine Learning with Python

$49.99

Machine Learning Engineering with Python

$49.99

Total $ 153.97

Valdez ladd Feb 13, 2024

The book has well written knowledge and resources for this subject. Hard to find so much structured information in one source. Thank you.

Feefo Verified review

Sarbjit Singh Hanjra Jul 29, 2024

I just finished reading "Interpretable Machine Learning with Python - Second Edition" Authored by Serg Masís and published by Packt.In the book, readers embark on a comprehensive journey through the intricate world of interpreting machine learning models. Authored with technical precision and practical insights, the book addresses the pressing need for understanding and explaining machine learning algorithms.The initial chapters lay a sturdy foundation, delineating the distinctions between interpretability and explainability while underscoring their significance in real-world applications. Through a compelling business case, readers grasp the imperative of interpretability in decision-making processes.Delving deeper, the book navigates through key concepts and challenges surrounding interpretation methodologies. From traditional model interpretations to the emergence of newer glass-box models, readers gain a nuanced understanding of interpretability paradigms.The narrative unfolds with an exploration of global and local model-agnostic interpretation methods, shedding light on feature importance and interactions. Anchors, counterfactual explanations, and visualization techniques offer multifaceted insights into model behaviors across various domains.The book extends its reach into the realms of convolutional neural networks (CNNs) and natural language processing (NLP) transformers, elucidating complex architectures through visualization and interpretation methods.Further chapters unravel the intricacies of multivariate forecasting, feature selection, bias mitigation, and causal inference methods, empowering readers to navigate through the interpretability landscape with finesse.Finally, discussions on model tuning, adversarial robustness, and future prospects in ML interpretability invite readers to contemplate the evolving role of transparency in machine learning systems."Interpretable Machine Learning with Python" emerges as an indispensable resource for practitioners, researchers, and enthusiasts alike, offering profound insights and actionable strategies to unravel the mysteries of machine learning models.

Amazon Verified review

Lydia Nov 12, 2023

An extremely valuable resource on the increasing important topic of knowing the how machine learning models make predictions. The descriptions of the methods and codes samples seamlessly join theory with practical application.The book starts with an excellent discussion of the levels of human understanding of a model and why understanding model biases is a key to successful modeling (from transparency to accountability and finally to fairness).Following chapters on performance metrics and what they reveal and what they do not see, the book dives into the tools to go beyond the precision, recall, and accuracy including tools for feature detection, counterfactual modeling, gradient based attribution methods, and graphic tools to better understanding the attribute weights found in transformer models.The book demonstrates different types of adversarial strategies can be used to undermine the model’s ability to make accurate predictions: vulnerabilities to different types of attacks is a key part of our understanding of how a model works.The book concludes with a discussion how model vulnerabilities and explainability are key parts of the path moving the technology forward to a fully mature useful tool with guardrails and standard safe practices.

Amazon Verified review

Saksham Dec 11, 2023

What I liked the most about this book is it’s explained concepts with the industry level problem statements. Overall it’s a great book

Amazon Verified review

David A Jacobs Dec 19, 2023

This ambitious book takes on three huge tasks: surveying tools and philosophies to help developers better interpret and explain AI models and their outcomes, documenting real-world cases of bias being built into data sets before offering strategies showing how not to repeat those mistakes, and offering a broad and deep state of the art of AI algorithms, a fast-moving field to say the least. Appropriate for its scope it’s a huge book, but it’s also a living one, with code examples available and an active Discord community in support of the material. I recommend this book for AI practitioners of all skill levels, as well as for students who want to get ahead of what will be one of the most important challenges we face over the decades to come.

Amazon Verified review

Interpretable Machine Learning with Python: Build explainable, fair, and robust high-performance models with hands-on, real-world examples , Second Edition

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