{"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":284,"pdf_page":300,"physical_index":300,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"da2afd66ae47ffa943f6fc0261c30e900069dd2a2784248fe7ba372e999b997f"},"chapter":"第9章","content":"$$\n\\mathrm { P r e c i s i o n } = { \\frac { T P } { T P + F P } } = { \\frac { 9 } { 9 + 3 } } = 0 . 7 5\n$$\n\n$$\n\\mathrm { R e c a l l } = { \\frac { T P } { T P + F N } } = { \\frac { 9 } { 9 + 2 } } = 0 . 8 2\n$$\n\n我们可能无法事先得知最合适的分类阈值，合理的做法是尝试使用许多不同的分类阈值来评估我们的模型。另外，有一个指标可以衡量模型在所有可能的分类阈值下的效果。该指标称为ROC曲线，即“接收者操作特征曲线”。\n\n9.4.5 ROC 曲线\n\nROC曲线（接收者操作特征曲线）是一种显示分类模型在所有分类阈值下的效果的图表。该曲线绘制了以下两个参数：\n\n●真正例率。\n\n●假正例率。\n\n真正例率（TPR）是召回率的同义词，因此定义如下：\n\n$$\nT P R = \\frac { T P } { T P + F N }\n$$\n\nTRP也叫灵敏度，表示所有正例中被分对的比例，衡量了分类器对正例的识别能力。假正例率（FPR）的定义如下：\n\n$$\nF P R = { \\frac { F P } { F P + T N } }\n$$\n\n假正例率计算的是分类器错认为正类的负实例占所有负实例的比例。\n\nROC曲线由两个变量绘制。横坐标是假正例率，纵坐标是真正例率。ROC曲线用于绘制采用不同分类阈值时的真正例率与假正例率。具体做法是:我们对每个可能的分类阈值进行评估，并观察相应阈值下的真正例率和假正例率。然后绘制一条曲线将这些点连接起来。图9-22显示了一个典型的ROC曲线。\n\n人工智能与大数据技术导论\n\n284","previous_page":283,"next_page":285}