{"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":229,"pdf_page":244,"physical_index":244,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"0cb475c9a6ae2ab1f8b74b6b8e7aa829173ef80e60a136143f7bde16e1f537bc"},"chapter":"内容简介","content":"假设测试集中包含有效邮件 8000 封，垃圾邮件 2000 封，共计10000 封邮件。我们的目标是找出所有的垃圾邮件。现在使用某个模型从中挑选出 5000 封垃圾邮件，经核实，模型挑选的垃圾邮件中有2000 封确实是垃圾邮件，但还错误地把 3000 封有效邮件也当作垃圾邮件挑选出来了。\n\n作为评估者，我们采用不同的指标来评估一下该模型的性能。首先我们知道垃圾邮件的识别模型是一个典型的 0-1 分类器，它的输出结果可能有以下四种：\n\n1）将垃圾邮件识别为垃圾邮件，这种 0-1 分类器将正面结果识别为真的称为“真正”（True Positive，TP）。\n\n2）将垃圾邮件识别为有效邮件，这种 0-1 分类器将正面结果识别为假的称为“假正”（False Positive，FP）。\n\n3）将有效邮件识别为垃圾邮件，这种 0-1 分类器将负面结果识别为假的称为“假反”（False Negative，FN）。\n\n4）将有效邮件识别为有效邮件，这种 0-1 分类器将负面结果识别为真的称为“真反”（True Negative，TN）。\n\n这四种结果所包含的预测值和实际值的关系，可以通过图 6-32 直观地反映出来。\n\n第 6 章 机器学习概览\n\n229","previous_page":228,"next_page":230}