{"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":195,"pdf_page":210,"physical_index":210,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"a9b917ef952301b95423c0dd94dd0d13461deabc765afce24de3bd91b6dca09d"},"chapter":"内容简介","content":"数据预处理阶段虽然是建模的准备阶段，是为建模服务的，但实际上在机器学习处理问题的过程中，这个阶段的工作量往往要占总工作量的一半甚至更多。并且它与建模及其他阶段并不是严格的先后顺序关系，而是贯穿在模型训练的全过程之中。有句圈子内常说的话：“数据和特征决定了机器学习的上限，而模型和算法只是逼近这个上限而已。”由此可见，数据预处理，尤其是特征选择在机器学习中占有相当重要的地位。\n\n6.6.5 损失函数\n\n完成数据预处理之后，便进入了电子邮件过滤系统的建模阶段。我们可以先从最简单的单一特征开始，构造一个最简单的垃圾邮件判别模型。假设我们选择以“收件人数量”这个特征为依据，对训练集中的 110 封电子邮件样例按照该特征进行统计，得到不同收件人数量的邮件样例分布，如图 6-9 所示。\n\n第 6 章 机器学习概览\n\n195","previous_page":194,"next_page":196}