{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","title":"智慧的疆界 从图灵机到人工智能","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787111778004","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","id":"cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","uuid":"cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","title":"智慧的疆界 从图灵机到人工智能","normalized_title":"智慧的疆界从图灵机到人工智能","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787111778004","isbns":["9787111778004","9787111695714"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":456,"navigation_url":"https://workshop.closeclaw.top/ai/book/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","title_url":"https://workshop.closeclaw.top/ai/title/%E6%99%BA%E6%85%A7%E7%9A%84%E7%96%86%E7%95%8C%20%E4%BB%8E%E5%9B%BE%E7%81%B5%E6%9C%BA%E5%88%B0%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","page_url":"https://workshop.closeclaw.top/api/ai/books/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb/page","search_url":"https://workshop.closeclaw.top/api/ai/books/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb/search"},"page":{"printed_page":230,"pdf_page":245,"physical_index":245,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"ba1eb0db0a4341d9a23b9542ecae470664d952f0ee44202f265643d34a1deea0"},"chapter":"内容简介","content":"有了“真正”“假正”“假反”和“真反”这四个概念，就可以给出“正确率”“精确率”和“召回率”的定义了。\n\n正确率是指对于给定的测试数据集，分类器正确分类的样本数与总样本数之比，即\n\n$$\n\\mathbb { E } \\oplus \\sharp \\sharp \\sharp \\frac { \\partial \\mathbb { E } \\oplus } { \\mathrm { ~ \\tiny ~ \\textnormal ~ { ~ T N ~ } ~ \\textnormal { ~ F N ~ } ~ \\textnormal { ~ T P ~ } ~ \\textnormal { ~ F P } ~ } }\n$$\n\n具体体现到我们预设的讨论场景中，正确率是此模型分辨正确的邮件数量占总邮件数量的比例，我们可知该模型判断正确的邮件是 7000 封（2000 封垃圾邮件、5000 封有效邮件），而总邮件数量是 10000 封，所以模型的正确率就是 70%，或者换句话说，模型的错误率是 30%。\n\n正确率是模型非常常用的指标之一，它很直观地反映了人们对模型能够“正确工作”的需求，对某些问题它能够衡量模型的性能，但是在另外一些场景里，它却会陷入“正确率悖论”（Accuracy Paradox）<sup>○一</sup>的尴尬之中。现在修改一下讨论场景中的数据，以便为读者展示什么是“正确率悖论”：假设测试集的 10000 封电子邮件中只包含 150 封垃圾邮件，其余都是有效邮件。在这个数据基础下，提供以下两个模型供读者择优选择：\n\n第一个模型从 10000 封邮件中识别出了 250 封垃圾邮件，而这 250 封被标记的垃圾邮件中，有 100 封确实是垃圾邮件，另外 150 封则是被错误标记的有效邮件，如表 6-2 所示。\n\n根据正确率的定义，易知在此测试集下，该模型的正确率是 98%，计算过程为：\n\n230\n\n第三部分 第三波高潮\n\n○一 正确率悖论的解释：https://en.wikipedia.org/wiki/Accuracy\\_paradox。","previous_page":229,"next_page":231}