Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples 9781800203907

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Автор: Masнs Serg
Название: Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples
ISBN: 9781800203907
Издательство: Packt Publishing
Классификация:

Машинное моделирование и имитация

Экспертные системы / системы баз знаний

Машинное обучение

Нейронные сети и нечеткие системы

ISBN-10: 180020390X
Обложка/Формат: Paperback
Страницы: 736
Вес: 1.24 кг.
Дата издания: 26.03.2021
Язык: English
Размер: 23.50 x 19.05 x 3.73 cm
Читательская аудитория: General (us: trade)
Подзаголовок: Learn to build interpretable high-performance models with hands-on real-world examples
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Поставляется из: США
Описание:

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 Na ve 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.

Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples, Masнs Serg