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

Masís, Serg

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商品描述

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 beginner's guide to interpretability, covering its relevance in business and exploring its key aspects and challenges. You'll 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, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you'll 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, you'll 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.

商品描述(中文翻譯)

了解機器學習可解釋性的關鍵方面和挑戰,學習如何通過解釋方法克服這些挑戰,並利用它們來建立更公平、更安全和更可靠的模型。

主要特點:
- 學習如何從任何機器學習模型中提取易於理解的洞察力
- 熟悉解釋性技術,以建立更公平、更安全和更可靠的模型
- 通過學習如何調試黑盒模型,減輕人工智能系統的風險

書籍描述:
你想理解模型並減輕使用機器學習(ML)解釋時的風險嗎?《Python可解釋機器學習》可以幫助你有效地使用ML模型。

本書的第一部分是解釋性的入門指南,介紹了它在商業中的相關性,並探討了其關鍵方面和挑戰。你將專注於白盒模型的工作原理,將其與黑盒和玻璃盒模型進行比較,並研究它們的權衡。第二部分將使你熟悉各種解釋方法,也稱為可解釋人工智能(XAI)方法,以及如何將它們應用於不同的用例,無論是用於分類還是回歸,用於表格、時間序列、圖像或文本。除了逐步的代碼,本書還通過示例幫助讀者解釋模型結果。在第三部分中,你將通過降低複雜性、減輕偏見、設置防護措施和增強可靠性,實際調整模型和訓練數據以實現可解釋性。你將探索的方法包括最先進的特徵選擇和數據集去偏方法,以及單調約束和對抗性重訓練。

通過閱讀本書,你將能夠更好地理解ML模型並通過解釋性調整來增強它們。

你將學到什麼:
- 認識解釋性在商業中的重要性
- 學習內在可解釋的模型,如線性模型、決策樹和朴素貝葉斯
- 熟練使用模型無關的方法來解釋模型
- 可視化圖像分類器的工作原理和學習內容
- 理解如何減輕數據集中偏見的影響
- 發現如何通過對抗性韌性使模型更可靠
- 使用單調約束來建立更公平和更安全的模型

本書適合數據科學家、機器學習開發人員和數據管理人員,他們對於解釋他們開發的AI系統的工作原理、對決策的影響以及如何識別和管理偏見具有越來越重要的責任。需要具備機器學習和Python編程語言的工作知識。