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《Interpretable Machine Learning》是少有的系统性地整理可解释性工作的图书。 书中每节介绍一种解释方法，既通过通俗易懂的语言直观地描述这种方法，也通过数学公式详细地介绍方法的理论，无论是对技术从业者还是对研究人员均大有裨益。 You will learn how to describe interpretable machine learning and differentiate between interpretability and explainability, explain and implement regression models in python, and demonstrate knowledge of generalized models in python. This survey is suitable as a starting point for statisticians and computer scientists interested in working in interpretable machine learning.
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本页面提供了关于可解释机器学习的翻译内容，帮助读者更好地理解相关概念和方法。 可解释的机器学习（翻译稿）介绍 本书介绍如何让有监督的机器学习模型可解释。这些章节包含一些数学公式，但即使没有公式，你也应当能够理解方法背后的思想。本书不适合那些试图从头学习机器学习的人。如果您不熟悉机器学习，有很多书籍和其他资源可供学习基础知识。我推荐Hastie. Who this book is for this book is essential for machine learning practitioners, data scientists, statisticians, and anyone interested in making their machine learning models interpretable
It will help readers select and apply the appropriate interpretation method for their specific project.
If so, then interpretable machine learning with python deserves a place on your bookshelf We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges.
