{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title":"人工智能与大数据技术导论","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787302517986","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","uuid":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title":"人工智能与大数据技术导论","normalized_title":"人工智能与大数据技术导论","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787302517986","isbns":["9787302517986"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":490,"navigation_url":"https://workshop.closeclaw.top/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title_url":"https://workshop.closeclaw.top/ai/title/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E4%B8%8E%E5%A4%A7%E6%95%B0%E6%8D%AE%E6%8A%80%E6%9C%AF%E5%AF%BC%E8%AE%BA","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","page_url":"https://workshop.closeclaw.top/api/ai/books/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page","search_url":"https://workshop.closeclaw.top/api/ai/books/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/search"},"page":{"printed_page":283,"pdf_page":299,"physical_index":299,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"6b86a21d8d31c5e593e89bc0e1bba504964706cf6ac43946a3862979fa968a14"},"chapter":"第9章","content":"召回率指的是实际垃圾邮件中正确分类的电子邮件所占的百分比，即图中阈值线右侧的绿点所占的百分比：\n\n$$\n{ \\mathrm { R e c a l l } } = { \\frac { T P } { T P + F N } } = { \\frac { 8 } { 8 + 3 } } = 0 . 7 3\n$$\n\n下面我们提高分类阈值，看看产生什么样的效果，如图9-18所示。\n\n假正例数量会减少，但假负例数量会相应地增加。结果，精确率有所提高，而召回率则有所降低，4种情况如图9-19所示。\n\n$$\n\\mathrm { P r e c i s i o n } = { \\frac { T P } { T P + F P } } = { \\frac { 7 } { 7 + 1 } } = 0 . 8 8\n$$\n\n$$\n\\mathrm { R e c a l l } = { \\frac { T P } { T P + F N } } = { \\frac { 7 } { 7 + 4 } } = 0 . 6 4\n$$\n\n如果我们降低分类阈值（从最初始位置开始），那么产生的效果是什么样的呢？如图9-20所示。\n\n假正例数量会增加，而假负例数量会减少。结果这一次，精确率有所降低，而召回率则有所提高，4种情况如图9-21所示。\n\n第9章 TensorFlow 高级知识\n\n283","previous_page":282,"next_page":284}