Interpretable Machine Learning
- 书名：Interpretable Machine Learning
- 作者： Christoph Molnar
- 格式：PDF
- 时间：2024-07-17
- 评分：
- ISBN：9780244768522
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.
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T-Lin06-14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
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simoncos|趙澈07-02工作需要用几天过了一遍，有点太拉杂（跟领域本身不成熟也有关系），对模型的介绍有重复，优缺点的讨论环节挺好的，公式部分头大，有些例子感觉真就只是例行公事，没能帮助进一步理解。NLP相关的东西比较少，回归分类以外的任务基本没提到
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捡面包屑鼠仔01-24扫了一遍 还是不戳哇
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