{"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":201,"pdf_page":216,"physical_index":216,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"c73adae7ff54220969068dd5700aac4d8b7c54c9b28867474ed553f64a7d187e"},"chapter":"内容简介","content":"6.6.6 模型选择\n\n如果只使用“收件人数量”这一个特征的话，最优的结果仅是在 110 封样本邮件中把错误归类的邮件减少到 20 封。显然这样的效果基本上没什么实用价值，它表明靠单个特征作为模型参数不足以建造出性能足够好的模型，因此我们还需要再引入一个或者更多的新特征来参与到模型构造之中，建造一个更高复杂度的模型，使其性能满足需要。\n\n这里仍然先把如何自动、合理地选择特征这个问题放一放，假设我们选择“邮件长度”作为第二个特征，用两个特征共同构造一个判别模型。那么以邮件长度作为统计维度，得出 110 封邮件的分布结果，如图 6-14 所示。\n\n图 6-14 反映了单独采用“邮件长度”作为判别是否垃圾邮件的依据的垃圾邮件分布结果，这个统计结果本身并没有什么可取之处，很明显单独使用邮件长度值作为判别依据也不能获得性能足够好的模型。不过，如果我们把这个统计结果与之前以收件人数量作为判别依据的统计结果联合起来，同时使用两个特征作为判别依据的话，可以得到一个与图 6-15 类似的统计图形，这个结\n\n第 6 章 机器学习概览\n\n201","previous_page":200,"next_page":202}