{"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":280,"pdf_page":296,"physical_index":296,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"f926dae2726f6ba203f97de5ae3cd23e0be335f108a8bbbb06b83aa48b78a469"},"chapter":"第9章","content":"人工智能与大数据技术导论\n\n●FalseNegatives（FN）：被错误地划分为负例的个数，即实际为正例但被分类器划分为负例的实例数。\n\nTrueNegatives（TN）：被正确地划分为负例的个数，即实际为负例且被分类器划分为负例的实例数。\n\n这里只要记住True、False描述的是分类器是否判断正确，Positive、Negative是分类器的分类结果。为了帮助大家理解，我们使用“狼来了”这则故事。假定“狼来了”是正类(PositiveClass)，那么“狼没来”就是负类（NegativeClass）。小男孩是一个牧童，狼来到镇上，如果他正确地指出来，就是真正例。他看到了狼并说“狼来了”。真正例对应的结果是小镇的羊得救，这很好。假正例则是小男孩说“狼来了”，但其实并没有狼。这就是假正例，这会令所有人非常恼火。假负例的后果可能更严重。假负例对应的情形是，狼来了而小男孩睡着了或没看到，狼进入镇子并吃掉了所有的羊。这可真的太惨了。真负例对应的情形是小男孩没喊“狼来了”，狼也确实没出现，一切安好。以上可以总结为如图9-14所示的4种情况。\n\n我们可以将这些预期情况组合成几个不同的指标。准确率（Accuracy）是我们最常见的评价指标，Accuracy=（TP+TN）/所有样本数，这个很容易理解，就是被分对的样本数除以所有的样本数。通常来说，准确率越高，分类器越好。所有样本数=TP+FP+FN+TN。下面试着计算一下图9-15中模型的准确率，该模型将100个肿瘤分为恶性（正类别）或良性（负类别）。\n\n$$\n\\mathrm { A c c u r a c y } = { \\frac { T P + T N } { T P + T N + F P + F N } } = { \\frac { 1 + 9 0 } { 1 + 9 0 + 1 + 8 } } = 0 . 9 1\n$$\n\n280","previous_page":279,"next_page":281}