9781800206571_

Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable models

Key Features

Learn how to extract easy-to-understand insights from any machine learning model
Become well-versed with interpretability techniques to build fairer, safer, and more reliable models
Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models

Book Description

Do you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models.

The first section of the book is a beginners guide to interpretability, covering its relevance in business and exploring its key aspects and challenges. Youll focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. The second section will get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, the book also helps the reader to interpret model outcomes using examples. In the third section, youll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods youll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining.

By the end of this book, youll be able to understand ML models better and enhance them through interpretability tuning.

What you will learn

Recognize the importance of interpretability in business
Study models that are intrinsically interpretable such as linear models, decision trees, and Naive Bayes
Become well-versed in interpreting models with model-agnostic methods
Visualize how an image classifier works and what it learns
Understand how to mitigate the influence of bias in datasets
Discover how to make models more reliable with adversarial robustness
Use monotonic constraints to make fairer and safer models

Who this book is for

This book is for data scientists, machine learning developers, and data stewards who have an increasingly critical responsibility to explain how the AI systems they develop work, their impact on decision making, and how they identify and manage bias. Working knowledge of machine learning and the Python programming language is expected.

(*_*)9781800206571_

A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models

Key Features

Learn how to extract easy-to-understand insights from any machine learning model
Become well-versed with interpretability techniques to build fairer, safer, and more reliable models
Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models

Book Description

Do you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. Well be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, youll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. Youll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. Youll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods youll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, youll be able to understand ML models better and enhance them through interpretability tuning.

What you will learn

Recognize the importance of interpretability in business
Study models that are intrinsically interpretable such as linear models, decision trees, and Nave Bayes
Become well-versed in interpreting models with model-agnostic methods
Visualize how an image classifier works and what it learns
Understand how to mitigate the influence of bias in datasets
Discover how to make models more reliable with adversarial robustness
Use monotonic constraints to make fairer and safer models

Who this book is for

This book is primarily written for data scientists, machine learning developers, and data stewards who find themselves under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. Its also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along.

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Interpretable Machine Learning with Python

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