May 2025
Intermediate to advanced
278 pages
3h 31m
Chinese
Overview
本作品已使用人工智能进行翻译。欢迎您提供反馈和意见：translation-feedback@oreilly.com
大多数中级机器学习书籍侧重于通过提高准确率或降低预测误差来优化模型。但这种方法往往忽视了理解机器学习模型为何以及如何做出特定预测的重要性。
可解释性方法为更好地理解模型行为提供了关键工具，而这本实用指南汇集了模型可解释性领域的顶尖技术。经验丰富的机器学习工程师和数据科学家将通过实践学习这些技术的工作原理，从而能够更轻松地在日常工作中应用这些工具。
这本必备书籍包含：
- 对一些最有用和最常用的可解释性技术进行详细探讨，突出其优缺点，帮助您选择最适合需求的工具
- 实施这些技术的实用技巧与最佳实践
- 与可解释性互动的指南，以及如何避免常见陷阱
- 将可解释性融入机器学习工作流所需的知识，以构建更健壮的机器学习系统
- 关于可解释人工智能技术的建议，包括如何将这些技术应用于处理表格、图像或文本数据的模型
- 使用知名可解释性库在 Keras 和 TensorFlow 2.0、PyTorch 以及 HuggingFace 中构建的模型在 Python 中的实现代码示例
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