{"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":99,"pdf_page":114,"physical_index":114,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"b078d4246484bc53172bfc1a136a075d1941e106b80eb1f89251ba8083ad7c36"},"chapter":"内容简介","content":"生活中类似的决策过程有很多，常见的如去医院看病，医生也是根据不同的检查数据，将病人的症状与疾病对应起来形成诊断结果，哪些症状可能由哪些疾病引发，是靠以前的病例总结归纳而来的。决策树学习算法的核心是从训练集中自动归纳出一组分类规则，使得这组规则不仅能适应已知的训练样本，对于与样本同分布的未知新数据也有一致的泛化性能。说白了，就是让计算机通过若干训练数据，自动构建出一棵分类规则最合理的决策树。\n\n对于如何从训练数据自动归纳出规则，即构建决策树的过程，目前已总结出一些成熟可行的算法，如 ID3 算法、C4.5/C5.0 算法、CART 算法、CHAID算法等，这些算法层面的内容对于本章来说过于具体了，等后面介绍机器学习时再讲，读者也可以很容易地在网上和教科书上找到这些经典算法的讲解，这里仅介绍一下这些算法构建决策树的共同思路。\n\n无论是 ID3、C4.5，还是其他决策树的构建算法，都有一个总的原则，即在每一步选择一个最好的属性来分裂树。此处“最好”的定义是：针对这个属性分裂出分支后，会使得各个分支节点中的训练集拥有尽可能高的纯度。\n\n第 3 章 符号主义学派\n\n99","previous_page":98,"next_page":100}