图书标签: 机器学习 计算机 Interpretable 计算机科学 美国 统计 MachineLearning En.
发表于2024-11-03
Interpretable Machine Learning pdf epub mobi txt 电子书 下载 2024
This book is about making machine learning models and their decisions interpretable.
After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME.
All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
On a mission to make algorithms more interpretable by combining machine learning and statistics.
写的狗屎一样,可解释性其实就是一个伪命题,可以看一下hinton对于可解释性的驳斥
评分重点在6-7章,https://christophm.github.io/interpretable-ml-book/
评分偏统计而非计算机
评分随着时间的推移模型的可解释性会越来越重要,或许是通过其他统计学方式来辅助,或许是推翻模型底层理论
评分解释有些理论并不是十分清楚,不过算是一本好书
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Interpretable Machine Learning pdf epub mobi txt 电子书 下载 2024