{"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":180,"pdf_page":196,"physical_index":196,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"8d159f1c79082786d095501e63ca07c41e58fa613d5d24c7c3b79ddc12c81f4f"},"chapter":"第6章","content":"1. CART 决策树模型\n\n每个节点可以选择的分界条件很多，比如 $^ { 4 \\prime } \\mathrm { X } 1 > 5 0 ^ { 9 \\cdot 3 } \\phantom { ^ { 4 } \\mathrm { X } 2 > 3 } ^ { 4 } \\mathrm { X } 3 = \\mathrm { F a l s e } ^ { 9 }$ 等。我们需要从所有分界点中选择给我们带来信息收益增量最大的一个。下面通过一个应用案例（鸢尾花种类识别）来说明CART算法。\n\n首先，读取iris数据，这里读取的是本地的CSV文件：\n\n回顾一下，这个数据集包括花瓣长度（PetalLenthCm）、花茎长度（SepalLenthCm）、花瓣宽度（PetalWidthCm）和花茎宽度（SepalWidthCm）四个连续型特征，一个包括3种分类类别的目标变量（Species），如图6-25所示。\n\n原始数据包括150个样本，我们将120个样本用于训练集，其余当作测试集：\n\n构建决策树：\n\ntree.DecisionTreeClassifier()是这个算法的核心模块。这里我们并没有在括号里向模型传递参数，所有参数均为默认值。DecisionTreeClassifier()可以接受以下arguments:\n\n树的最大优点在于可视化的直观性。我们可以用graphviz程序包进行树模型的可视化。要使用这个工具，就要在控制台中安装 graphviz。以Anaconda prompt为例，打开Anaconda prompt,输入如下指令：\n\n人工智能与大数据技术导论\n\n180","previous_page":179,"next_page":181}