{"id":94948,"date":"2026-08-15T19:32:56","date_gmt":"2026-08-15T11:32:56","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/94948.html"},"modified":"2026-08-15T19:32:56","modified_gmt":"2026-08-15T11:32:56","slug":"%e7%ac%ac21%e8%af%be%ef%bc%9ascikit-learn%ef%bd%9c%e5%86%b3%e7%ad%96%e6%a0%91%e5%88%86%e7%b1%bb%e5%8e%9f%e7%90%86%e3%80%81%e5%9f%ba%e5%b0%bc%e7%b3%bb%e6%95%b0%e3%80%81%e4%bf%a1%e6%81%af%e7%86%b5","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/94948.html","title":{"rendered":"\u7b2c21\u8bfe\uff1ascikit-learn\uff5c\u51b3\u7b56\u6811\u5206\u7c7b\u539f\u7406\u3001\u57fa\u5c3c\u7cfb\u6570\u3001\u4fe1\u606f\u71b5\u4e0e\u526a\u679d\u7b56\u7565"},"content":{"rendered":"<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/08\/20260815113253-6a804e653e10d.jpg\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<\/p>\n<h4>\u6587\u7ae0\u76ee\u5f55<\/h4>\n<ul>\n<li>\n<ul>\n<li>\u8bfe\u524d\u5bfc\u8bfb<\/li>\n<li>\u5b66\u4e60\u76ee\u6807<\/li>\n<li>\u77e5\u8bc6\u70b9\u7406\u8bba\u8bb2\u89e3<\/li>\n<li>\n<ul>\n<li>\u4e00\u3001\u51b3\u7b56\u6811\u7684\u57fa\u672c\u6982\u5ff5<\/li>\n<li>\u4e8c\u3001\u5206\u88c2\u51c6\u5219&#xff1a;\u5982\u4f55\u9009\u62e9\u6700\u4f73\u7279\u5f81&#xff1f;<\/li>\n<li>\n<ul>\n<li>1. \u57fa\u5c3c\u7cfb\u6570&#xff08;Gini Impurity&#xff09;<\/li>\n<li>2. \u4fe1\u606f\u71b5&#xff08;Information Entropy&#xff09;<\/li>\n<li>3. \u4fe1\u606f\u589e\u76ca<\/li>\n<\/ul>\n<\/li>\n<li>\u4e09\u3001\u51b3\u7b56\u6811\u7684\u505c\u6b62\u6761\u4ef6\u4e0e\u526a\u679d<\/li>\n<li>\u56db\u3001\u51b3\u7b56\u6811\u7684\u4f18\u7f3a\u70b9<\/li>\n<\/ul>\n<\/li>\n<li>\u6838\u5fc3\u539f\u7406\u901a\u4fd7\u62c6\u89e3<\/li>\n<li>\n<ul>\n<li>\u51b3\u7b56\u6811&#xff1a;20\u4e2a\u95ee\u9898\u6e38\u620f<\/li>\n<li>\u57fa\u5c3c\u7cfb\u6570 vs \u4fe1\u606f\u71b5<\/li>\n<\/ul>\n<\/li>\n<li>\u5e95\u5c42\u6570\u5b66\u903b\u8f91<\/li>\n<li>\n<ul>\n<li>1. \u4e8c\u5206\u7c7b\u60c5\u51b5\u4e0b\u7684\u57fa\u5c3c\u7cfb\u6570<\/li>\n<li>2. \u4fe1\u606f\u589e\u76ca\u7684\u8ba1\u7b97\u793a\u4f8b<\/li>\n<li>3. \u4ee3\u4ef7\u590d\u6742\u5ea6\u526a\u679d<\/li>\n<\/ul>\n<\/li>\n<li>scikit-learn API\u8be6\u89e3<\/li>\n<li>\n<ul>\n<li>DecisionTreeClassifier<\/li>\n<\/ul>\n<\/li>\n<li>\u73af\u5883\u914d\u7f6e\u4e0e\u4f9d\u8d56\u5b89\u88c5<\/li>\n<li>\u5b8c\u6574\u4ee3\u7801\u5b9e\u6218&#xff08;\u5e26\u8be6\u7ec6\u6ce8\u91ca&#xff09;<\/li>\n<li>\n<ul>\n<li>\u5b9e\u62181&#xff1a;\u51b3\u7b56\u6811\u57fa\u7840\u2014\u2014\u9e22\u5c3e\u82b1\u5206\u7c7b\u4e0e\u53ef\u89c6\u5316<\/li>\n<li>\u5b9e\u62182&#xff1a;\u57fa\u5c3c\u7cfb\u6570 vs \u4fe1\u606f\u71b5\u5bf9\u6bd4<\/li>\n<li>\u5b9e\u62183&#xff1a;\u9884\u526a\u679d\u53c2\u6570\u5bf9\u8fc7\u62df\u5408\u7684\u5f71\u54cd<\/li>\n<li>\u5b9e\u62184&#xff1a;\u7279\u5f81\u91cd\u8981\u6027\u5206\u6790<\/li>\n<li>\u5b9e\u62185&#xff1a;\u540e\u526a\u679d\u2014\u2014\u4ee3\u4ef7\u590d\u6742\u5ea6\u526a\u679d&#xff08;CCP&#xff09;<\/li>\n<li>\u5b9e\u62186&#xff1a;\u591a\u53c2\u6570\u7f51\u683c\u641c\u7d22&#xff08;\u9884\u526a\u679d &#043; \u540e\u526a\u679d&#xff09;<\/li>\n<li>\u5b9e\u62187&#xff1a;\u51b3\u7b56\u6811\u56de\u5f52&#xff08;DecisionTreeRegressor&#xff09;<\/li>\n<\/ul>\n<\/li>\n<li>\u6848\u4f8b\u5b9e\u64cd\u6f14\u793a<\/li>\n<li>\n<ul>\n<li>\u6848\u4f8b&#xff1a;\u4fe1\u7528\u5361\u8fdd\u7ea6\u9884\u6d4b&#xff08;\u51b3\u7b56\u6811\u4e8c\u5206\u7c7b&#xff09;<\/li>\n<\/ul>\n<\/li>\n<li>\u5e38\u89c1\u62a5\u9519\u4e0e\u907f\u5751\u6307\u5357<\/li>\n<li>\n<ul>\n<li>\u62a5\u95191&#xff1a;&#096;ValueError: min_samples_split must be an integer&#096;<\/li>\n<li>\u62a5\u95192&#xff1a;\u6811\u592a\u5927&#xff0c;&#096;plot_tree&#096; \u663e\u793a\u6a21\u7cca<\/li>\n<li>\u62a5\u95193&#xff1a;\u7279\u5f81\u91cd\u8981\u6027\u5168\u4e3a\u96f6\u6216\u67d0\u4e9b\u7279\u5f81\u91cd\u8981\u6027\u5f02\u5e38<\/li>\n<li>\u62a5\u95194&#xff1a;&#096;RuntimeError: Tree is too large&#096; \u5728\u53ef\u89c6\u5316\u65f6<\/li>\n<li>\u907f\u5751\u603b\u7ed3<\/li>\n<\/ul>\n<\/li>\n<li>\u77e5\u8bc6\u70b9\u603b\u7ed3<\/li>\n<li>\n<ul>\n<li>\u539f\u7406<\/li>\n<li>API<\/li>\n<li>\u53ef\u89c6\u5316\u4e0e\u89e3\u91ca<\/li>\n<li>\u6269\u5c55<\/li>\n<\/ul>\n<\/li>\n<li>\u8bfe\u540e\u7ec3\u4e60\u9898<\/li>\n<li>\n<ul>\n<li>\u9009\u62e9\u9898<\/li>\n<li>\u586b\u7a7a\u9898<\/li>\n<li>\u5b9e\u64cd\u9898<\/li>\n<li>\u601d\u8003\u9898<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<li>&#x1f517;\u300a30\u8282\u8bfe scikit-learn \u4ece\u5165\u95e8\u5230\u7cbe\u901a\u300b\u7cfb\u5217\u8bfe\u7a0b\u5bfc\u822a<\/li>\n<\/ul>\n<hr \/>\n<h3>\u8bfe\u524d\u5bfc\u8bfb<\/h3>\n<p>\u6b22\u8fce\u6765\u5230\u7b2c21\u8bfe&#xff01;\u5982\u679c\u4f60\u95ee\u201c\u6700\u50cf\u4eba\u7c7b\u51b3\u7b56\u8fc7\u7a0b\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u662f\u4ec0\u4e48\u201d&#xff0c;\u7b54\u6848\u4e00\u5b9a\u662f\u51b3\u7b56\u6811\u3002\u60f3\u8c61\u533b\u751f\u8bca\u65ad\u75c5\u60c5&#xff1a;\u4f53\u6e29\u8d85\u8fc738\u00b0C\u5417&#xff1f;\u54b3\u55fd\u5417&#xff1f;\u8840\u5e38\u89c4\u767d\u7ec6\u80de\u9ad8\u5417&#xff1f;\u6bcf\u6b21\u56de\u7b54\u201c\u662f\/\u5426\u201d\u5c31\u6cbf\u7740\u6811\u5f80\u4e0b\u8d70&#xff0c;\u6700\u7ec8\u5230\u8fbe\u4e00\u4e2a\u7ed3\u8bba&#xff08;\u6d41\u611f\u3001\u666e\u901a\u611f\u5192\u6216\u5176\u4ed6&#xff09;\u3002\u51b3\u7b56\u6811\u5c31\u662f\u6a21\u4eff\u8fd9\u4e00\u8fc7\u7a0b\u2014\u2014\u4ece\u6839\u8282\u70b9\u5f00\u59cb&#xff0c;\u6bcf\u4e2a\u5185\u90e8\u8282\u70b9\u662f\u4e00\u4e2a\u7279\u5f81\u4e0a\u7684\u6d4b\u8bd5&#xff08;\u5982\u201c\u5e74\u9f84&lt;30&#xff1f;\u201d&#xff09;&#xff0c;\u6bcf\u4e2a\u5206\u652f\u662f\u6d4b\u8bd5\u7ed3\u679c&#xff0c;\u53f6\u5b50\u8282\u70b9\u662f\u7c7b\u522b\u6807\u7b7e\u3002<\/p>\n<p>\u51b3\u7b56\u6811\u7684\u4f18\u70b9\u662f\u5929\u7136\u53ef\u89e3\u91ca&#xff0c;\u4e0d\u9700\u8981\u201c\u9ed1\u7bb1\u201d&#xff0c;\u4f60\u53ef\u4ee5\u76f4\u89c2\u5730\u770b\u5230\u4e3a\u4ec0\u4e48\u67d0\u4e2a\u6837\u672c\u88ab\u5206\u7c7b\u5230\u67d0\u4e00\u7c7b\u3002\u672c\u8bfe\u5c06\u8be6\u7ec6\u8bb2\u89e3\u51b3\u7b56\u6811\u7684\u6784\u5efa\u6838\u5fc3&#xff1a;\u5982\u4f55\u9009\u62e9\u6700\u4f73\u5206\u88c2\u7279\u5f81&#xff1f;\u5e38\u7528\u7684\u8861\u91cf\u6807\u51c6\u6709\u57fa\u5c3c\u7cfb\u6570\u548c\u4fe1\u606f\u71b5&#xff0c;\u4e24\u8005\u90fd\u80fd\u8bc4\u4f30\u5b50\u8282\u70b9\u7684\u201c\u7eaf\u5ea6\u201d\u3002\u4f60\u8fd8\u4f1a\u5b66\u5230\u5982\u4f55\u901a\u8fc7\u9650\u5236\u6811\u7684\u9ad8\u5ea6\u3001\u8282\u70b9\u6700\u5c11\u6837\u672c\u6570\u7b49\u53c2\u6570\u8fdb\u884c\u9884\u526a\u679d&#xff0c;\u4ee5\u53ca\u901a\u8fc7\u540e\u526a\u679d&#xff08;ccp_alpha&#xff09;\u8fdb\u4e00\u6b65\u538b\u7f29\u6811\u7684\u7ed3\u6784&#xff0c;\u9632\u6b62\u8fc7\u62df\u5408\u3002\u901a\u8fc7\u53ef\u89c6\u5316\u51b3\u7b56\u6811\u548c\u63d0\u53d6\u7279\u5f81\u91cd\u8981\u6027&#xff0c;\u4f60\u5c06\u80fd\u5411\u4e1a\u52a1\u4eba\u5458\u6e05\u6670\u5730\u89e3\u91ca\u6a21\u578b\u51b3\u7b56\u8fc7\u7a0b\u3002<\/p>\n<h3>\u5b66\u4e60\u76ee\u6807<\/h3>\n<p>\u5b8c\u6210\u672c\u8bfe\u5b66\u4e60\u540e&#xff0c;\u4f60\u5c06\u80fd\u591f&#xff1a;<\/p>\n<li>\u89e3\u91ca \u51b3\u7b56\u6811\u7684\u6784\u5efa\u8fc7\u7a0b&#xff1a;\u4ece\u6839\u8282\u70b9\u5f00\u59cb&#xff0c;\u9012\u5f52\u9009\u62e9\u6700\u4f73\u7279\u5f81\u5206\u88c2&#xff0c;\u76f4\u81f3\u6ee1\u8db3\u505c\u6b62\u6761\u4ef6<\/li>\n<li>\u7406\u89e3 \u57fa\u5c3c\u7cfb\u6570\u548c\u4fe1\u606f\u71b5\u7684\u6570\u5b66\u5b9a\u4e49\u53ca\u5176\u4f5c\u4e3a\u5206\u88c2\u51c6\u5219\u7684\u4f5c\u7528<\/li>\n<li>\u4f7f\u7528 DecisionTreeClassifier \u8fdb\u884c\u8bad\u7ec3\u3001\u9884\u6d4b\u548c\u8bc4\u4f30<\/li>\n<li>\u638c\u63e1 \u5173\u952e\u53c2\u6570&#xff1a;criterion&#xff08;\u2018gini\u2019\/\u2018entropy\u2019&#xff09;\u3001max_depth\u3001min_samples_split\u3001min_samples_leaf\u3001max_features\u3001ccp_alpha<\/li>\n<li>\u53ef\u89c6\u5316 \u51b3\u7b56\u6811\u7ed3\u6784&#xff08;plot_tree \u6216 export_graphviz&#xff09;<\/li>\n<li>\u63d0\u53d6 \u7279\u5f81\u91cd\u8981\u6027&#xff0c;\u5206\u6790\u54ea\u4e9b\u7279\u5f81\u5bf9\u5206\u7c7b\u8d21\u732e\u6700\u5927<\/li>\n<li>\u5b9e\u65bd \u9884\u526a\u679d\u548c\u540e\u526a\u679d&#xff08;\u4ee3\u4ef7\u590d\u6742\u5ea6\u526a\u679d&#xff09;\u6765\u9632\u6b62\u8fc7\u62df\u5408<\/li>\n<h3>\u77e5\u8bc6\u70b9\u7406\u8bba\u8bb2\u89e3<\/h3>\n<h4>\u4e00\u3001\u51b3\u7b56\u6811\u7684\u57fa\u672c\u6982\u5ff5<\/h4>\n<p>\u51b3\u7b56\u6811\u662f\u4e00\u79cd\u6811\u5f62\u7ed3\u6784\u7684\u5206\u7c7b\u6a21\u578b&#xff0c;\u7531\u4ee5\u4e0b\u5143\u7d20\u7ec4\u6210&#xff1a;<\/p>\n<ul>\n<li>\u6839\u8282\u70b9&#xff1a;\u5305\u542b\u5168\u90e8\u6837\u672c&#xff0c;\u662f\u5206\u88c2\u7684\u8d77\u70b9\u3002<\/li>\n<li>\u5185\u90e8\u8282\u70b9&#xff1a;\u5bf9\u5e94\u4e00\u4e2a\u7279\u5f81\u4e0a\u7684\u6d4b\u8bd5&#xff08;\u5982 X[2] &lt;&#061; 1.5&#xff09;\u3002<\/li>\n<li>\u5206\u652f&#xff1a;\u6d4b\u8bd5\u7ed3\u679c\u7684\u8f93\u51fa&#xff08;\u5982\u201c\u662f\/\u5426\u201d\u6216\u591a\u79cd\u53d6\u503c&#xff09;\u3002<\/li>\n<li>\u53f6\u5b50\u8282\u70b9&#xff1a;\u6700\u7ec8\u5206\u7c7b\u7ed3\u679c&#xff08;\u7c7b\u522b\u6807\u7b7e&#xff09;\u3002<\/li>\n<\/ul>\n<p>\u6784\u5efa\u51b3\u7b56\u6811\u7684\u8fc7\u7a0b\u662f\u4e00\u4e2a\u9012\u5f52\u5212\u5206\u8fc7\u7a0b&#xff1a;\u6bcf\u6b21\u9009\u62e9\u4e00\u4e2a\u7279\u5f81\u548c\u9608\u503c&#xff0c;\u5c06\u5f53\u524d\u8282\u70b9\u6837\u672c\u96c6\u5212\u5206\u4e3a\u4e24\u4e2a&#xff08;\u6216\u591a\u4e2a&#xff09;\u5b50\u96c6&#xff0c;\u4f7f\u5f97\u5b50\u96c6\u4e2d\u7684\u6837\u672c\u201c\u7eaf\u5ea6\u201d\u5c3d\u53ef\u80fd\u9ad8\u3002\u5f53\u5b50\u96c6\u5df2\u7ecf\u7eaf\u51c0&#xff08;\u5168\u90e8\u540c\u4e00\u7c7b&#xff09;\u6216\u6ee1\u8db3\u505c\u6b62\u6761\u4ef6\u65f6&#xff0c;\u505c\u6b62\u5212\u5206\u3002<\/p>\n<h4>\u4e8c\u3001\u5206\u88c2\u51c6\u5219&#xff1a;\u5982\u4f55\u9009\u62e9\u6700\u4f73\u7279\u5f81&#xff1f;<\/h4>\n<p>\u5206\u88c2\u7684\u76ee\u6807\u662f\u8ba9\u5212\u5206\u540e\u7684\u5b50\u8282\u70b9\u201c\u6700\u7eaf\u201d&#xff0c;\u5373\u5305\u542b\u5c3d\u53ef\u80fd\u591a\u540c\u4e00\u7c7b\u522b\u7684\u6837\u672c\u3002\u8861\u91cf\u7eaf\u5ea6\u7684\u6307\u6807\u6709&#xff1a;<\/p>\n<h5>1. \u57fa\u5c3c\u7cfb\u6570&#xff08;Gini Impurity&#xff09;<\/h5>\n<p>\u8861\u91cf\u4e00\u4e2a\u8282\u70b9\u4e2d\u968f\u673a\u62bd\u53d6\u4e24\u4e2a\u6837\u672c\u7c7b\u522b\u4e0d\u4e00\u81f4\u7684\u6982\u7387\u3002\u8282\u70b9 <span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>         t <\/p>\n<p>        t <\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.6151em\"><\/span><span class=\"mord mathnormal\">t<\/span><\/span><\/span><\/span><\/span> \u7684\u57fa\u5c3c\u7cfb\u6570\u4e3a&#xff1a;<\/p>\n<p><span class=\"katex--display\"><span class=\"katex-display\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>          G <\/p>\n<p>          i <\/p>\n<p>          n <\/p>\n<p>          i <\/p>\n<p>          ( <\/p>\n<p>          t <\/p>\n<p>          ) <\/p>\n<p>          &#061; <\/p>\n<p>          1 <\/p>\n<p>          \u2212 <\/p>\n<p>           \u2211 <\/p>\n<p>            k <\/p>\n<p>            &#061; <\/p>\n<p>            1 <\/p>\n<p>           K <\/p>\n<p>           p <\/p>\n<p>            t <\/p>\n<p>            k <\/p>\n<p>           2 <\/p>\n<p>         Gini(t) &#061; 1 &#8211; \\\\sum_{k&#061;1}^{K} p_{tk}^2 <\/p>\n<p>     <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord mathnormal\">G<\/span><span class=\"mord mathnormal\">ini<\/span><span class=\"mopen\">(<\/span><span class=\"mord mathnormal\">t<\/span><span class=\"mclose\">)<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.7278em;vertical-align: -0.0833em\"><\/span><span class=\"mord\">1<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">\u2212<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 3.1304em;vertical-align: -1.3021em\"><\/span><span class=\"mop op-limits\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 1.8283em\"><span class=\"\" style=\"top: -1.8479em;margin-left: 0em\"><span class=\"pstrut\" style=\"height: 3.05em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\" style=\"margin-right: 0.0315em\">k<\/span><span class=\"mrel mtight\">&#061;<\/span><span class=\"mord mtight\">1<\/span><\/span><\/span><\/span><span class=\"\" style=\"top: -3.05em\"><span class=\"pstrut\" style=\"height: 3.05em\"><\/span><span class=\"\"><span class=\"mop op-symbol large-op\">\u2211<\/span><\/span><\/span><span class=\"\" style=\"top: -4.3em;margin-left: 0em\"><span class=\"pstrut\" style=\"height: 3.05em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\" style=\"margin-right: 0.0715em\">K<\/span><\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 1.3021em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.1667em\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\">p<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.8641em\"><span class=\"\" style=\"top: -2.453em;margin-left: 0em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\">t<\/span><span class=\"mord mathnormal mtight\" style=\"margin-right: 0.0315em\">k<\/span><\/span><\/span><\/span><span class=\"\" style=\"top: -3.113em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\">2<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.247em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>\u5176\u4e2d <span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>          p <\/p>\n<p>           t <\/p>\n<p>           k <\/p>\n<p>        p_{tk} <\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em;vertical-align: -0.1944em\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\">p<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3361em\"><span class=\"\" style=\"top: -2.55em;margin-left: 0em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\">t<\/span><span class=\"mord mathnormal mtight\" style=\"margin-right: 0.0315em\">k<\/span><\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u662f\u8282\u70b9 <span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>         t <\/p>\n<p>        t <\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.6151em\"><\/span><span class=\"mord mathnormal\">t<\/span><\/span><\/span><\/span><\/span> \u4e2d\u7b2c <span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>         k <\/p>\n<p>        k <\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.6944em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0315em\">k<\/span><\/span><\/span><\/span><\/span> \u7c7b\u6837\u672c\u7684\u6bd4\u4f8b\u3002\u57fa\u5c3c\u7cfb\u6570\u8d8a\u5c0f&#xff0c;\u8282\u70b9\u8d8a\u7eaf&#xff08;\u5f53\u6240\u6709\u6837\u672c\u5c5e\u4e8e\u540c\u4e00\u7c7b\u65f6&#xff0c;Gini&#061;0&#xff09;\u3002<\/p>\n<p>\u5bf9\u4e8e\u4e8c\u5206\u7c7b&#xff0c;<span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>         G <\/p>\n<p>         i <\/p>\n<p>         n <\/p>\n<p>         i <\/p>\n<p>         &#061; <\/p>\n<p>         1 <\/p>\n<p>         \u2212 <\/p>\n<p>         ( <\/p>\n<p>          p <\/p>\n<p>          2 <\/p>\n<p>         &#043; <\/p>\n<p>         ( <\/p>\n<p>         1 <\/p>\n<p>         \u2212 <\/p>\n<p>         p <\/p>\n<p>          ) <\/p>\n<p>          2 <\/p>\n<p>         ) <\/p>\n<p>         &#061; <\/p>\n<p>         2 <\/p>\n<p>         p <\/p>\n<p>         ( <\/p>\n<p>         1 <\/p>\n<p>         \u2212 <\/p>\n<p>         p <\/p>\n<p>         ) <\/p>\n<p>        Gini &#061; 1 &#8211; (p^2 &#043; (1-p)^2) &#061; 2p(1-p) <\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.6833em\"><\/span><span class=\"mord mathnormal\">G<\/span><span class=\"mord mathnormal\">ini<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.7278em;vertical-align: -0.0833em\"><\/span><span class=\"mord\">1<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">\u2212<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1.0641em;vertical-align: -0.25em\"><\/span><span class=\"mopen\">(<\/span><span class=\"mord\"><span class=\"mord mathnormal\">p<\/span><span class=\"msupsub\"><span class=\"vlist-t\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.8141em\"><span class=\"\" style=\"top: -3.063em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\">2<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">&#043;<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mopen\">(<\/span><span class=\"mord\">1<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">\u2212<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1.0641em;vertical-align: -0.25em\"><\/span><span class=\"mord mathnormal\">p<\/span><span class=\"mclose\"><span class=\"mclose\">)<\/span><span class=\"msupsub\"><span class=\"vlist-t\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.8141em\"><span class=\"\" style=\"top: -3.063em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\">2<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mclose\">)<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord\">2<\/span><span class=\"mord mathnormal\">p<\/span><span class=\"mopen\">(<\/span><span class=\"mord\">1<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">\u2212<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord mathnormal\">p<\/span><span class=\"mclose\">)<\/span><\/span><\/span><\/span><\/span>&#xff0c;\u6700\u5927\u503c\u5728 p&#061;0.5 \u65f6\u4e3a 0.5\u3002<\/p>\n<h5>2. \u4fe1\u606f\u71b5&#xff08;Information Entropy&#xff09;<\/h5>\n<p>\u71b5\u4e5f\u5ea6\u91cf\u4e0d\u786e\u5b9a\u6027&#xff1a;<\/p>\n<p><span class=\"katex--display\"><span class=\"katex-display\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>          E <\/p>\n<p>          n <\/p>\n<p>          t <\/p>\n<p>          r <\/p>\n<p>          o <\/p>\n<p>          p <\/p>\n<p>          y <\/p>\n<p>          ( <\/p>\n<p>          t <\/p>\n<p>          ) <\/p>\n<p>          &#061; <\/p>\n<p>          \u2212 <\/p>\n<p>           \u2211 <\/p>\n<p>            k <\/p>\n<p>            &#061; <\/p>\n<p>            1 <\/p>\n<p>           K <\/p>\n<p>           p <\/p>\n<p>            t <\/p>\n<p>            k <\/p>\n<p>            log <\/p>\n<p>            \u2061 <\/p>\n<p>           2 <\/p>\n<p>           p <\/p>\n<p>            t <\/p>\n<p>            k <\/p>\n<p>         Entropy(t) &#061; -\\\\sum_{k&#061;1}^{K} p_{tk} \\\\log_2 p_{tk} <\/p>\n<p>     <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0576em\">E<\/span><span class=\"mord mathnormal\">n<\/span><span class=\"mord mathnormal\">t<\/span><span class=\"mord mathnormal\">ro<\/span><span class=\"mord mathnormal\">p<\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0359em\">y<\/span><span class=\"mopen\">(<\/span><span class=\"mord mathnormal\">t<\/span><span class=\"mclose\">)<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 3.1304em;vertical-align: -1.3021em\"><\/span><span class=\"mord\">\u2212<\/span><span class=\"mspace\" style=\"margin-right: 0.1667em\"><\/span><span class=\"mop op-limits\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 1.8283em\"><span class=\"\" style=\"top: -1.8479em;margin-left: 0em\"><span class=\"pstrut\" style=\"height: 3.05em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\" style=\"margin-right: 0.0315em\">k<\/span><span class=\"mrel mtight\">&#061;<\/span><span class=\"mord mtight\">1<\/span><\/span><\/span><\/span><span class=\"\" style=\"top: -3.05em\"><span class=\"pstrut\" style=\"height: 3.05em\"><\/span><span class=\"\"><span class=\"mop op-symbol large-op\">\u2211<\/span><\/span><\/span><span class=\"\" style=\"top: -4.3em;margin-left: 0em\"><span class=\"pstrut\" style=\"height: 3.05em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\" style=\"margin-right: 0.0715em\">K<\/span><\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 1.3021em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.1667em\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\">p<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3361em\"><span class=\"\" style=\"top: -2.55em;margin-left: 0em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\">t<\/span><span class=\"mord mathnormal mtight\" style=\"margin-right: 0.0315em\">k<\/span><\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.1667em\"><\/span><span class=\"mop\"><span class=\"mop\">lo<span style=\"margin-right: 0.0139em\">g<\/span><\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.207em\"><span class=\"\" style=\"top: -2.4559em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\">2<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.2441em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.1667em\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\">p<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3361em\"><span class=\"\" style=\"top: -2.55em;margin-left: 0em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\">t<\/span><span class=\"mord mathnormal mtight\" style=\"margin-right: 0.0315em\">k<\/span><\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>\u5f53\u8282\u70b9\u7eaf\u65f6&#xff0c;\u71b5&#061;0&#xff1b;\u5f53\u5404\u7c7b\u7b49\u6bd4\u4f8b\u65f6&#xff0c;\u71b5\u6700\u5927&#xff08;\u4e8c\u5206\u7c7b\u65f6\u6700\u5927\u4e3a1&#xff09;\u3002<\/p>\n<h5>3. \u4fe1\u606f\u589e\u76ca<\/h5>\n<p>\u9009\u62e9\u5206\u88c2\u7279\u5f81\u65f6&#xff0c;\u6211\u4eec\u8ba1\u7b97\u5206\u88c2\u524d\u7684\u71b5&#xff08;\u6216\u57fa\u5c3c&#xff09;\u4e0e\u5206\u88c2\u540e\u5b50\u8282\u70b9\u71b5\u7684\u52a0\u6743\u5e73\u5747\u503c\u4e4b\u95f4\u7684\u5dee\u5f02&#xff0c;\u5373\u4fe1\u606f\u589e\u76ca&#xff1a;<\/p>\n<p><span class=\"katex--display\"><span class=\"katex-display\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>          I <\/p>\n<p>          G <\/p>\n<p>          &#061; <\/p>\n<p>          E <\/p>\n<p>          n <\/p>\n<p>          t <\/p>\n<p>          r <\/p>\n<p>          o <\/p>\n<p>          p <\/p>\n<p>          y <\/p>\n<p>          ( <\/p>\n<p>          p <\/p>\n<p>          a <\/p>\n<p>          r <\/p>\n<p>          e <\/p>\n<p>          n <\/p>\n<p>          t <\/p>\n<p>          ) <\/p>\n<p>          \u2212 <\/p>\n<p>           \u2211 <\/p>\n<p>           j <\/p>\n<p>            n <\/p>\n<p>            j <\/p>\n<p>           n <\/p>\n<p>          E <\/p>\n<p>          n <\/p>\n<p>          t <\/p>\n<p>          r <\/p>\n<p>          o <\/p>\n<p>          p <\/p>\n<p>          y <\/p>\n<p>          ( <\/p>\n<p>          c <\/p>\n<p>          h <\/p>\n<p>          i <\/p>\n<p>          l <\/p>\n<p>           d <\/p>\n<p>           j <\/p>\n<p>          ) <\/p>\n<p>         IG &#061; Entropy(parent) &#8211; \\\\sum_{j} \\\\frac{n_j}{n} Entropy(child_j) <\/p>\n<p>     <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.6833em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0785em\">I<\/span><span class=\"mord mathnormal\">G<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0576em\">E<\/span><span class=\"mord mathnormal\">n<\/span><span class=\"mord mathnormal\">t<\/span><span class=\"mord mathnormal\">ro<\/span><span class=\"mord mathnormal\">p<\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0359em\">y<\/span><span class=\"mopen\">(<\/span><span class=\"mord mathnormal\">p<\/span><span class=\"mord mathnormal\">a<\/span><span class=\"mord mathnormal\">re<\/span><span class=\"mord mathnormal\">n<\/span><span class=\"mord mathnormal\">t<\/span><span class=\"mclose\">)<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">\u2212<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 2.5213em;vertical-align: -1.4138em\"><\/span><span class=\"mop op-limits\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 1.05em\"><span class=\"\" style=\"top: -1.8723em;margin-left: 0em\"><span class=\"pstrut\" style=\"height: 3.05em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\" style=\"margin-right: 0.0572em\">j<\/span><\/span><\/span><\/span><span class=\"\" style=\"top: -3.05em\"><span class=\"pstrut\" style=\"height: 3.05em\"><\/span><span class=\"\"><span class=\"mop op-symbol large-op\">\u2211<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 1.4138em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.1667em\"><\/span><span class=\"mord\"><span class=\"mopen nulldelimiter\"><\/span><span class=\"mfrac\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 1.1076em\"><span class=\"\" style=\"top: -2.314em\"><span class=\"pstrut\" style=\"height: 3em\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\">n<\/span><\/span><\/span><span class=\"\" style=\"top: -3.23em\"><span class=\"pstrut\" style=\"height: 3em\"><\/span><span class=\"frac-line\" style=\"border-bottom-width: 0.04em\"><\/span><\/span><span class=\"\" style=\"top: -3.677em\"><span class=\"pstrut\" style=\"height: 3em\"><\/span><span class=\"mord\"><span class=\"mord\"><span class=\"mord mathnormal\">n<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3117em\"><span class=\"\" style=\"top: -2.55em;margin-left: 0em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\" style=\"margin-right: 0.0572em\">j<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.2861em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.686em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><span class=\"mclose nulldelimiter\"><\/span><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0576em\">E<\/span><span class=\"mord mathnormal\">n<\/span><span class=\"mord mathnormal\">t<\/span><span class=\"mord mathnormal\">ro<\/span><span class=\"mord mathnormal\">p<\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0359em\">y<\/span><span class=\"mopen\">(<\/span><span class=\"mord mathnormal\">c<\/span><span class=\"mord mathnormal\">hi<\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0197em\">l<\/span><span class=\"mord\"><span class=\"mord mathnormal\">d<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3117em\"><span class=\"\" style=\"top: -2.55em;margin-left: 0em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\" style=\"margin-right: 0.0572em\">j<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.2861em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mclose\">)<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>\u9009\u62e9\u4f7f\u4fe1\u606f\u589e\u76ca\u6700\u5927\u7684\u7279\u5f81\u3002<\/p>\n<p>\u5728 scikit-learn \u4e2d&#xff0c;criterion&#061;&#039;gini&#039; \u4f7f\u7528\u57fa\u5c3c\u7cfb\u6570&#xff0c;criterion&#061;&#039;entropy&#039; \u4f7f\u7528\u4fe1\u606f\u71b5\u3002\u901a\u5e38\u4e24\u8005\u5dee\u5f02\u4e0d\u5927&#xff0c;\u57fa\u5c3c\u7cfb\u6570\u8ba1\u7b97\u7a0d\u5feb&#xff0c;\u4fe1\u606f\u71b5\u5bf9\u5206\u5e03\u66f4\u654f\u611f\u3002<\/p>\n<h4>\u4e09\u3001\u51b3\u7b56\u6811\u7684\u505c\u6b62\u6761\u4ef6\u4e0e\u526a\u679d<\/h4>\n<p>\u5982\u679c\u4e0d\u52a0\u9650\u5236&#xff0c;\u51b3\u7b56\u6811\u4f1a\u4e00\u76f4\u5206\u88c2\u76f4\u5230\u6bcf\u4e2a\u53f6\u5b50\u8282\u70b9\u90fd\u662f\u7eaf\u7684&#xff0c;\u8fd9\u4f1a\u5bfc\u81f4\u8fc7\u62df\u5408\u2014\u2014\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u5b8c\u7f8e\u5206\u7c7b&#xff0c;\u4f46\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8868\u73b0\u5dee\u3002<\/p>\n<p>\u9884\u526a\u679d&#xff1a;\u5728\u6784\u5efa\u8fc7\u7a0b\u4e2d\u63d0\u524d\u505c\u6b62\u5206\u88c2&#xff0c;\u4f8b\u5982&#xff1a;<\/p>\n<ul>\n<li>max_depth&#xff1a;\u9650\u5236\u6811\u7684\u6700\u5927\u6df1\u5ea6\u3002<\/li>\n<li>min_samples_split&#xff1a;\u8282\u70b9\u81f3\u5c11\u5305\u542b\u591a\u5c11\u6837\u672c\u624d\u5206\u88c2\u3002<\/li>\n<li>min_samples_leaf&#xff1a;\u53f6\u5b50\u8282\u70b9\u6700\u5c11\u6837\u672c\u6570\u3002<\/li>\n<li>min_impurity_decrease&#xff1a;\u5206\u88c2\u5e26\u6765\u7684\u7eaf\u5ea6\u63d0\u5347\u81f3\u5c11\u8fbe\u5230\u9608\u503c\u3002<\/li>\n<\/ul>\n<p>\u540e\u526a\u679d&#xff1a;\u5148\u8ba9\u6811\u5145\u5206\u751f\u957f&#xff0c;\u7136\u540e\u81ea\u5e95\u5411\u4e0a\u526a\u53bb\u4e0d\u91cd\u8981\u7684\u5206\u652f\u3002scikit-learn \u63d0\u4f9b\u4ee3\u4ef7\u590d\u6742\u5ea6\u526a\u679d&#xff08;Cost Complexity Pruning, CCP&#xff09;&#xff0c;\u901a\u8fc7\u53c2\u6570 ccp_alpha \u63a7\u5236\u526a\u679d\u5f3a\u5ea6\u3002ccp_alpha \u8d8a\u5927&#xff0c;\u526a\u679d\u8d8a\u5f3a&#xff0c;\u6811\u8d8a\u5c0f\u3002<\/p>\n<h4>\u56db\u3001\u51b3\u7b56\u6811\u7684\u4f18\u7f3a\u70b9<\/h4>\n<table>\n<tr>\u4f18\u70b9\u7f3a\u70b9<\/tr>\n<tbody>\n<tr>\n<td>\u6613\u4e8e\u7406\u89e3\u548c\u89e3\u91ca&#xff0c;\u53ef\u53ef\u89c6\u5316<\/td>\n<td>\u5bb9\u6613\u8fc7\u62df\u5408&#xff08;\u9700\u526a\u679d&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u4e0d\u9700\u8981\u7279\u5f81\u7f29\u653e&#xff08;\u6811\u6a21\u578b\u5bf9\u5c3a\u5ea6\u4e0d\u654f\u611f&#xff09;<\/td>\n<td>\u5bf9\u6570\u636e\u5fae\u5c0f\u53d8\u5316\u654f\u611f&#xff08;\u65b9\u5dee\u5927&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u53ef\u5904\u7406\u6570\u503c\u548c\u7c7b\u522b\u7279\u5f81<\/td>\n<td>\u53ef\u80fd\u4ea7\u751f\u6709\u504f\u6811&#xff08;\u5f53\u67d0\u4e9b\u7279\u5f81\u53d6\u503c\u591a\u65f6&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u9690\u5f0f\u8fdb\u884c\u7279\u5f81\u9009\u62e9<\/td>\n<td>\u5b66\u4e60\u80fd\u529b\u6709\u9650&#xff08;\u4e0d\u5982\u96c6\u6210\u6a21\u578b&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u53ef\u8f93\u51fa\u7279\u5f81\u91cd\u8981\u6027<\/td>\n<td>\u5916\u63a8\u80fd\u529b\u5dee&#xff08;\u4e0d\u80fd\u9884\u6d4b\u8d85\u51fa\u8bad\u7ec3\u96c6\u8303\u56f4\u7684\u503c&#xff09;<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>\u6838\u5fc3\u539f\u7406\u901a\u4fd7\u62c6\u89e3<\/h3>\n<h4>\u51b3\u7b56\u6811&#xff1a;20\u4e2a\u95ee\u9898\u6e38\u620f<\/h4>\n<p>\u60f3\u8c61\u4f60\u5728\u73a9\u201c20\u4e2a\u95ee\u9898\u201d\u6e38\u620f&#xff1a;\u5fc3\u91cc\u60f3\u4e00\u4e2a\u52a8\u7269&#xff0c;\u5bf9\u65b9\u901a\u8fc7\u95ee\u201c\u662f\/\u5426\u201d\u95ee\u9898\u6765\u731c\u3002\u4f8b\u5982\u201c\u5b83\u662f\u5426\u751f\u6d3b\u5728\u6c34\u4e2d&#xff1f;\u201d\u3001\u201c\u662f\u5426\u6709\u7fbd\u6bdb&#xff1f;\u201d\u3001\u201c\u662f\u5426\u5403\u8089&#xff1f;\u201d\u3002\u6bcf\u6b21\u95ee\u9898\u90fd\u5c06\u53ef\u80fd\u6027\u7a7a\u95f4\u4e00\u5206\u4e3a\u4e8c&#xff0c;\u76f4\u5230\u9501\u5b9a\u7b54\u6848\u3002\u51b3\u7b56\u6811\u5c31\u662f\u8fd9\u79cd\u63d0\u95ee\u7b56\u7565\u7684\u6570\u5b66\u5316&#xff1a;\u9009\u62e9\u54ea\u4e2a\u95ee\u9898\u5148\u95ee&#xff08;\u6839\u8282\u70b9&#xff09;\u80fd\u4f7f\u4e0d\u786e\u5b9a\u6027\u964d\u4f4e\u6700\u5feb&#xff0c;\u8fd9\u5c31\u662f\u4fe1\u606f\u589e\u76ca\u3002<\/p>\n<h4>\u57fa\u5c3c\u7cfb\u6570 vs \u4fe1\u606f\u71b5<\/h4>\n<p>\u57fa\u5c3c\u7cfb\u6570\u53ef\u4ee5\u7406\u89e3\u4e3a\u201c\u9519\u8bef\u5206\u7c7b\u7684\u98ce\u9669\u201d\u3002\u5982\u679c\u8282\u70b9\u4e2d80%\u662f\u732b&#xff0c;20%\u662f\u72d7&#xff0c;\u90a3\u4e48\u968f\u673a\u731c\u4e00\u4e2a\u662f\u732b\u7684\u9519\u8bef\u6982\u7387\u662f20%&#xff08;\u5047\u8bbe\u731c\u591a\u6570\u7c7b&#xff09;\u3002\u57fa\u5c3c\u7cfb\u6570\u7a0d\u5fae\u590d\u6742\u4e9b&#xff0c;\u4f46\u6838\u5fc3\u601d\u60f3\u7c7b\u4f3c\u3002\u4fe1\u606f\u71b5\u5219\u6765\u81ea\u4e8e\u4fe1\u606f\u8bba&#xff0c;\u8861\u91cf\u201c\u4e0d\u786e\u5b9a\u6027\u201d&#xff0c;\u4e24\u8005\u5728\u5927\u591a\u6570\u60c5\u51b5\u4e0b\u8868\u73b0\u76f8\u4f3c\u3002<\/p>\n<h3>\u5e95\u5c42\u6570\u5b66\u903b\u8f91<\/h3>\n<h4>1. \u4e8c\u5206\u7c7b\u60c5\u51b5\u4e0b\u7684\u57fa\u5c3c\u7cfb\u6570<\/h4>\n<p>\u8bbe\u8282\u70b9\u4e2d\u6b63\u4f8b\u6bd4\u4f8b <span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>         p <\/p>\n<p>        p <\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em;vertical-align: -0.1944em\"><\/span><span class=\"mord mathnormal\">p<\/span><\/span><\/span><\/span><\/span>&#xff0c;\u8d1f\u4f8b\u6bd4\u4f8b <span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>         1 <\/p>\n<p>         \u2212 <\/p>\n<p>         p <\/p>\n<p>        1-p <\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.7278em;vertical-align: -0.0833em\"><\/span><span class=\"mord\">1<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">\u2212<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em;vertical-align: -0.1944em\"><\/span><span class=\"mord mathnormal\">p<\/span><\/span><\/span><\/span><\/span>&#xff1a; <span class=\"katex--display\"><span class=\"katex-display\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>          G <\/p>\n<p>          i <\/p>\n<p>          n <\/p>\n<p>          i <\/p>\n<p>          &#061; <\/p>\n<p>          1 <\/p>\n<p>          \u2212 <\/p>\n<p>          ( <\/p>\n<p>           p <\/p>\n<p>           2 <\/p>\n<p>          &#043; <\/p>\n<p>          ( <\/p>\n<p>          1 <\/p>\n<p>          \u2212 <\/p>\n<p>          p <\/p>\n<p>           ) <\/p>\n<p>           2 <\/p>\n<p>          ) <\/p>\n<p>          &#061; <\/p>\n<p>          2 <\/p>\n<p>          p <\/p>\n<p>          ( <\/p>\n<p>          1 <\/p>\n<p>          \u2212 <\/p>\n<p>          p <\/p>\n<p>          ) <\/p>\n<p>         Gini &#061; 1 &#8211; (p^2 &#043; (1-p)^2) &#061; 2p(1-p) <\/p>\n<p>     <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.6833em\"><\/span><span class=\"mord mathnormal\">G<\/span><span class=\"mord mathnormal\">ini<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.7278em;vertical-align: -0.0833em\"><\/span><span class=\"mord\">1<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">\u2212<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1.1141em;vertical-align: -0.25em\"><\/span><span class=\"mopen\">(<\/span><span class=\"mord\"><span class=\"mord mathnormal\">p<\/span><span class=\"msupsub\"><span class=\"vlist-t\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.8641em\"><span class=\"\" style=\"top: -3.113em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\">2<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">&#043;<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mopen\">(<\/span><span class=\"mord\">1<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">\u2212<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1.1141em;vertical-align: -0.25em\"><\/span><span class=\"mord mathnormal\">p<\/span><span class=\"mclose\"><span class=\"mclose\">)<\/span><span class=\"msupsub\"><span class=\"vlist-t\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.8641em\"><span class=\"\" style=\"top: -3.113em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\">2<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mclose\">)<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord\">2<\/span><span class=\"mord mathnormal\">p<\/span><span class=\"mopen\">(<\/span><span class=\"mord\">1<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">\u2212<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord mathnormal\">p<\/span><span class=\"mclose\">)<\/span><\/span><\/span><\/span><\/span><\/span> \u5f53 <span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>         p <\/p>\n<p>         &#061; <\/p>\n<p>         0 <\/p>\n<p>        p&#061;0 <\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em;vertical-align: -0.1944em\"><\/span><span class=\"mord mathnormal\">p<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.6444em\"><\/span><span class=\"mord\">0<\/span><\/span><\/span><\/span><\/span> \u6216 <span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>         p <\/p>\n<p>         &#061; <\/p>\n<p>         1 <\/p>\n<p>        p&#061;1 <\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em;vertical-align: -0.1944em\"><\/span><span class=\"mord mathnormal\">p<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.6444em\"><\/span><span class=\"mord\">1<\/span><\/span><\/span><\/span><\/span> \u65f6&#xff0c;Gini&#061;0&#xff1b;<span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>         p <\/p>\n<p>         &#061; <\/p>\n<p>         0.5 <\/p>\n<p>        p&#061;0.5 <\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em;vertical-align: -0.1944em\"><\/span><span class=\"mord mathnormal\">p<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.6444em\"><\/span><span class=\"mord\">0.5<\/span><\/span><\/span><\/span><\/span> \u65f6&#xff0c;Gini&#061;0.5\u3002<\/p>\n<h4>2. \u4fe1\u606f\u589e\u76ca\u7684\u8ba1\u7b97\u793a\u4f8b<\/h4>\n<p>\u5047\u8bbe\u7236\u8282\u70b9\u670910\u4e2a\u6b63\u4f8b\u300110\u4e2a\u8d1f\u4f8b&#xff0c;\u71b5 &#061; -0.5log\u20820.5 -0.5log\u20820.5 &#061; 1\u3002\u6309\u7279\u5f81A\u5206\u6210\u4e24\u4e2a\u5b50\u8282\u70b9&#xff1a;\u5de6\u5b50\u8282\u70b95\u6b631\u8d1f&#xff08;\u71b5&#061; -5\/6 log\u20825\/6 -1\/6 log\u20821\/6 \u2248 0.65&#xff09;&#xff0c;\u53f3\u5b50\u8282\u70b95\u6b639\u8d1f&#xff08;\u71b5 \u2248 0.94&#xff09;\u3002\u52a0\u6743\u5e73\u5747\u71b5 &#061; (6\/20)*0.65 &#043; (14\/20)*0.94 \u2248 0.85\u3002\u4fe1\u606f\u589e\u76ca &#061; 1 &#8211; 0.85 &#061; 0.15\u3002<\/p>\n<h4>3. \u4ee3\u4ef7\u590d\u6742\u5ea6\u526a\u679d<\/h4>\n<p>CCP \u5b9a\u4e49\u5b50\u6811\u7684\u4ee3\u4ef7\u590d\u6742\u5ea6\u4e3a <span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>          R <\/p>\n<p>          \u03b1 <\/p>\n<p>         ( <\/p>\n<p>         T <\/p>\n<p>         ) <\/p>\n<p>         &#061; <\/p>\n<p>         R <\/p>\n<p>         ( <\/p>\n<p>         T <\/p>\n<p>         ) <\/p>\n<p>         &#043; <\/p>\n<p>         \u03b1 <\/p>\n<p>         \u2223 <\/p>\n<p>          T <\/p>\n<p>          ~ <\/p>\n<p>         \u2223 <\/p>\n<p>        R_\\\\alpha(T) &#061; R(T) &#043; \\\\alpha |\\\\tilde{T}| <\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right: 0.0077em\">R<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.1514em\"><span class=\"\" style=\"top: -2.55em;margin-left: -0.0077em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\" style=\"margin-right: 0.0037em\">\u03b1<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mopen\">(<\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.1389em\">T<\/span><span class=\"mclose\">)<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0077em\">R<\/span><span class=\"mopen\">(<\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.1389em\">T<\/span><span class=\"mclose\">)<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">&#043;<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1.1702em;vertical-align: -0.25em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0037em\">\u03b1<\/span><span class=\"mord\">\u2223<\/span><span class=\"mord accent\"><span class=\"vlist-t\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.9202em\"><span class=\"\" style=\"top: -3em\"><span class=\"pstrut\" style=\"height: 3em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.1389em\">T<\/span><\/span><span class=\"\" style=\"top: -3.6023em\"><span class=\"pstrut\" style=\"height: 3em\"><\/span><span class=\"accent-body\" style=\"left: -0.1667em\"><span class=\"mord\">~<\/span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mord\">\u2223<\/span><\/span><\/span><\/span><\/span>&#xff0c;\u5176\u4e2d <span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>         R <\/p>\n<p>         ( <\/p>\n<p>         T <\/p>\n<p>         ) <\/p>\n<p>        R(T) <\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0077em\">R<\/span><span class=\"mopen\">(<\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.1389em\">T<\/span><span class=\"mclose\">)<\/span><\/span><\/span><\/span><\/span> \u662f\u6811\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u8bef\u5dee&#xff08;\u5982\u57fa\u5c3c\u4e0d\u7eaf\u5ea6&#xff09;&#xff0c;<span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>         \u2223 <\/p>\n<p>          T <\/p>\n<p>          ~ <\/p>\n<p>         \u2223 <\/p>\n<p>        |\\\\tilde{T}| <\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 1.1702em;vertical-align: -0.25em\"><\/span><span class=\"mord\">\u2223<\/span><span class=\"mord accent\"><span class=\"vlist-t\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.9202em\"><span class=\"\" style=\"top: -3em\"><span class=\"pstrut\" style=\"height: 3em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.1389em\">T<\/span><\/span><span class=\"\" style=\"top: -3.6023em\"><span class=\"pstrut\" style=\"height: 3em\"><\/span><span class=\"accent-body\" style=\"left: -0.1667em\"><span class=\"mord\">~<\/span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mord\">\u2223<\/span><\/span><\/span><\/span><\/span> \u662f\u53f6\u5b50\u8282\u70b9\u6570&#xff0c;<span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>         \u03b1 <\/p>\n<p>        \\\\alpha <\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.4306em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0037em\">\u03b1<\/span><\/span><\/span><\/span><\/span> \u662f\u60e9\u7f5a\u7cfb\u6570\u3002\u5bf9\u4e8e\u6bcf\u4e2a <span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>         \u03b1 <\/p>\n<p>        \\\\alpha <\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.4306em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0037em\">\u03b1<\/span><\/span><\/span><\/span><\/span>&#xff0c;\u53ef\u4ee5\u627e\u5230\u6700\u5c0f\u5316\u8be5\u635f\u5931\u7684\u6700\u4f18\u5b50\u6811\u3002<span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>         \u03b1 <\/p>\n<p>        \\\\alpha <\/p>\n<p>    <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.4306em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0037em\">\u03b1<\/span><\/span><\/span><\/span><\/span> \u8d8a\u5927&#xff0c;\u6811\u8d8a\u5c0f\u3002<\/p>\n<h3>scikit-learn API\u8be6\u89e3<\/h3>\n<h4>DecisionTreeClassifier<\/h4>\n<p><span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>tree <span class=\"token keyword\">import<\/span> DecisionTreeClassifier<\/p>\n<p>dt <span class=\"token operator\">&#061;<\/span> DecisionTreeClassifier<span class=\"token punctuation\">(<\/span><br \/>\n    criterion<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;gini&#039;<\/span><span class=\"token punctuation\">,<\/span>           <span class=\"token comment\"># \u5206\u88c2\u51c6\u5219: &#039;gini&#039; \u6216 &#039;entropy&#039;<\/span><br \/>\n    splitter<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;best&#039;<\/span><span class=\"token punctuation\">,<\/span>            <span class=\"token comment\"># &#039;best&#039; \u6216 &#039;random&#039;<\/span><br \/>\n    max_depth<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">None<\/span><span class=\"token punctuation\">,<\/span>             <span class=\"token comment\"># \u6811\u7684\u6700\u5927\u6df1\u5ea6<\/span><br \/>\n    min_samples_split<span class=\"token operator\">&#061;<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span>        <span class=\"token comment\"># \u5185\u90e8\u8282\u70b9\u518d\u5206\u88c2\u6240\u9700\u6700\u5c11\u6837\u672c\u6570<\/span><br \/>\n    min_samples_leaf<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span>         <span class=\"token comment\"># \u53f6\u5b50\u8282\u70b9\u6700\u5c11\u6837\u672c\u6570<\/span><br \/>\n    min_weight_fraction_leaf<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.0<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    max_features<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">None<\/span><span class=\"token punctuation\">,<\/span>          <span class=\"token comment\"># \u5bfb\u627e\u6700\u4f73\u5206\u88c2\u65f6\u8003\u8651\u7684\u7279\u5f81\u6570<\/span><br \/>\n    random_state<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">None<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    max_leaf_nodes<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">None<\/span><span class=\"token punctuation\">,<\/span>        <span class=\"token comment\"># \u6700\u5927\u53f6\u5b50\u8282\u70b9\u6570<\/span><br \/>\n    min_impurity_decrease<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.0<\/span><span class=\"token punctuation\">,<\/span>  <span class=\"token comment\"># \u5206\u88c2\u6240\u9700\u7684\u6700\u5c0f\u4e0d\u7eaf\u5ea6\u964d\u4f4e<\/span><br \/>\n    class_weight<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">None<\/span><span class=\"token punctuation\">,<\/span>          <span class=\"token comment\"># \u7c7b\u522b\u6743\u91cd&#xff08;\u5904\u7406\u4e0d\u5e73\u8861&#xff09;<\/span><br \/>\n    ccp_alpha<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.0<\/span>               <span class=\"token comment\"># \u540e\u526a\u679d\u590d\u6742\u5ea6\u53c2\u6570&#xff08;&gt;&#061;0&#xff09;<\/span><br \/>\n<span class=\"token punctuation\">)<\/span><\/p>\n<p>\u5e38\u7528\u5c5e\u6027&#xff1a;<\/p>\n<ul>\n<li>feature_importances_&#xff1a;\u7279\u5f81\u91cd\u8981\u6027&#xff08;\u5f52\u4e00\u5316\u540e\u548c\u4e3a1&#xff09;<\/li>\n<li>tree_&#xff1a;\u5e95\u5c42Tree\u5bf9\u8c61&#xff0c;\u53ef\u83b7\u53d6\u6811\u7ed3\u6784<\/li>\n<li>classes_\u3001n_classes_\u3001n_features_in_<\/li>\n<\/ul>\n<p>\u65b9\u6cd5&#xff1a;fit, predict, predict_proba, score<\/p>\n<p>\u53ef\u89c6\u5316&#xff1a;<\/p>\n<p><span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>tree <span class=\"token keyword\">import<\/span> plot_tree<br \/>\nplot_tree<span class=\"token punctuation\">(<\/span>dt<span class=\"token punctuation\">,<\/span> filled<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">,<\/span> feature_names<span class=\"token operator\">&#061;<\/span>feature_names<span class=\"token punctuation\">,<\/span> class_names<span class=\"token operator\">&#061;<\/span>class_names<span class=\"token punctuation\">)<\/span><\/p>\n<h3>\u73af\u5883\u914d\u7f6e\u4e0e\u4f9d\u8d56\u5b89\u88c5<\/h3>\n<p>\u672c\u8bfe\u9700\u8981\u5b89\u88c5 graphviz&#xff08;\u7528\u4e8e export_graphviz \u9ad8\u7ea7\u53ef\u89c6\u5316&#xff0c;\u53ef\u9009&#xff09;\u3002\u57fa\u672c\u7ed8\u56fe\u53ef\u7528 plot_tree\u3002<\/p>\n<p>conda activate sklearn_tutorial<br \/>\npip <span class=\"token function\">install<\/span> graphviz  <span class=\"token comment\"># \u53ef\u9009<\/span><\/p>\n<h3>\u5b8c\u6574\u4ee3\u7801\u5b9e\u6218&#xff08;\u5e26\u8be6\u7ec6\u6ce8\u91ca&#xff09;<\/h3>\n<h4>\u5b9e\u62181&#xff1a;\u51b3\u7b56\u6811\u57fa\u7840\u2014\u2014\u9e22\u5c3e\u82b1\u5206\u7c7b\u4e0e\u53ef\u89c6\u5316<\/h4>\n<p><span class=\"token comment\"># -*- coding: utf-8 -*-<\/span><br \/>\n<span class=\"token triple-quoted-string string\">&#034;&#034;&#034;<br \/>\n\u51b3\u7b56\u6811\u5206\u7c7b&#xff1a;\u9e22\u5c3e\u82b1\u6570\u636e\u96c6&#xff0c;\u53ef\u89c6\u5316\u6811\u7ed3\u6784<br \/>\n&#034;&#034;&#034;<\/span><\/p>\n<p><span class=\"token keyword\">import<\/span> matplotlib<span class=\"token punctuation\">.<\/span>pyplot <span class=\"token keyword\">as<\/span> plt<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>datasets <span class=\"token keyword\">import<\/span> load_iris<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>model_selection <span class=\"token keyword\">import<\/span> train_test_split<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>tree <span class=\"token keyword\">import<\/span> DecisionTreeClassifier<span class=\"token punctuation\">,<\/span> plot_tree<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>metrics <span class=\"token keyword\">import<\/span> accuracy_score<span class=\"token punctuation\">,<\/span> confusion_matrix<br \/>\n<span class=\"token keyword\">import<\/span> seaborn <span class=\"token keyword\">as<\/span> sns<\/p>\n<p><span class=\"token comment\"># \u52a0\u8f7d\u6570\u636e<\/span><br \/>\niris <span class=\"token operator\">&#061;<\/span> load_iris<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\nX<span class=\"token punctuation\">,<\/span> y <span class=\"token operator\">&#061;<\/span> iris<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">,<\/span> iris<span class=\"token punctuation\">.<\/span>target<br \/>\nfeature_names <span class=\"token operator\">&#061;<\/span> iris<span class=\"token punctuation\">.<\/span>feature_names<br \/>\nclass_names <span class=\"token operator\">&#061;<\/span> iris<span class=\"token punctuation\">.<\/span>target_names<\/p>\n<p>X_train<span class=\"token punctuation\">,<\/span> X_test<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">,<\/span> y_test <span class=\"token operator\">&#061;<\/span> train_test_split<span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">,<\/span> test_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.2<\/span><span class=\"token punctuation\">,<\/span> random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u8bad\u7ec3\u51b3\u7b56\u6811&#xff08;\u4e0d\u526a\u679d&#xff09;<\/span><br \/>\ndt <span class=\"token operator\">&#061;<\/span> DecisionTreeClassifier<span class=\"token punctuation\">(<\/span>random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><br \/>\ndt<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">)<\/span><\/p>\n<p>y_pred <span class=\"token operator\">&#061;<\/span> dt<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>X_test<span class=\"token punctuation\">)<\/span><br \/>\nacc <span class=\"token operator\">&#061;<\/span> accuracy_score<span class=\"token punctuation\">(<\/span>y_test<span class=\"token punctuation\">,<\/span> y_pred<span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u51b3\u7b56\u6811\u51c6\u786e\u7387: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>acc<span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u6811\u7684\u6df1\u5ea6: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>dt<span class=\"token punctuation\">.<\/span>tree_<span class=\"token punctuation\">.<\/span>max_depth<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u53f6\u5b50\u8282\u70b9\u6570: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>dt<span class=\"token punctuation\">.<\/span>tree_<span class=\"token punctuation\">.<\/span>n_leaves<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u53ef\u89c6\u5316\u51b3\u7b56\u6811<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>figure<span class=\"token punctuation\">(<\/span>figsize<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">20<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplot_tree<span class=\"token punctuation\">(<\/span>dt<span class=\"token punctuation\">,<\/span> filled<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">,<\/span> feature_names<span class=\"token operator\">&#061;<\/span>feature_names<span class=\"token punctuation\">,<\/span> class_names<span class=\"token operator\">&#061;<\/span>class_names<span class=\"token punctuation\">,<\/span> rounded<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>title<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\u9e22\u5c3e\u82b1\u51b3\u7b56\u6811&#xff08;\u672a\u526a\u679d&#xff09;&#034;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>show<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u6df7\u6dc6\u77e9\u9635<\/span><br \/>\ncm <span class=\"token operator\">&#061;<\/span> confusion_matrix<span class=\"token punctuation\">(<\/span>y_test<span class=\"token punctuation\">,<\/span> y_pred<span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>figure<span class=\"token punctuation\">(<\/span>figsize<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">5<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">4<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\nsns<span class=\"token punctuation\">.<\/span>heatmap<span class=\"token punctuation\">(<\/span>cm<span class=\"token punctuation\">,<\/span> annot<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">,<\/span> fmt<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;d&#039;<\/span><span class=\"token punctuation\">,<\/span> cmap<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;Blues&#039;<\/span><span class=\"token punctuation\">,<\/span> xticklabels<span class=\"token operator\">&#061;<\/span>class_names<span class=\"token punctuation\">,<\/span> yticklabels<span class=\"token operator\">&#061;<\/span>class_names<span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>title<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u6df7\u6dc6\u77e9\u9635&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>xlabel<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u9884\u6d4b&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>ylabel<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u771f\u5b9e&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>show<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>\u5b9e\u62182&#xff1a;\u57fa\u5c3c\u7cfb\u6570 vs \u4fe1\u606f\u71b5\u5bf9\u6bd4<\/h4>\n<p><span class=\"token comment\"># -*- coding: utf-8 -*-<\/span><br \/>\n<span class=\"token triple-quoted-string string\">&#034;&#034;&#034;<br \/>\n\u5bf9\u6bd4 criterion&#061;&#039;gini&#039; \u548c &#039;entropy&#039; \u5728\u4e0d\u540c\u6570\u636e\u96c6\u4e0a\u7684\u8868\u73b0<br \/>\n\u4f7f\u7528\u7ea2\u9152\u6570\u636e\u96c6<br \/>\n&#034;&#034;&#034;<\/span><\/p>\n<p><span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>datasets <span class=\"token keyword\">import<\/span> load_wine<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>model_selection <span class=\"token keyword\">import<\/span> cross_val_score<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>tree <span class=\"token keyword\">import<\/span> DecisionTreeClassifier<br \/>\n<span class=\"token keyword\">import<\/span> numpy <span class=\"token keyword\">as<\/span> np<\/p>\n<p>wine <span class=\"token operator\">&#061;<\/span> load_wine<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\nX<span class=\"token punctuation\">,<\/span> y <span class=\"token operator\">&#061;<\/span> wine<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">,<\/span> wine<span class=\"token punctuation\">.<\/span>target<\/p>\n<p><span class=\"token comment\"># 5\u6298\u4ea4\u53c9\u9a8c\u8bc1<\/span><br \/>\ncv <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">5<\/span><br \/>\nscores_gini <span class=\"token operator\">&#061;<\/span> cross_val_score<span class=\"token punctuation\">(<\/span>DecisionTreeClassifier<span class=\"token punctuation\">(<\/span>criterion<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;gini&#039;<\/span><span class=\"token punctuation\">,<\/span> random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">,<\/span> cv<span class=\"token operator\">&#061;<\/span>cv<span class=\"token punctuation\">)<\/span><br \/>\nscores_entropy <span class=\"token operator\">&#061;<\/span> cross_val_score<span class=\"token punctuation\">(<\/span>DecisionTreeClassifier<span class=\"token punctuation\">(<\/span>criterion<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;entropy&#039;<\/span><span class=\"token punctuation\">,<\/span> random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">,<\/span> cv<span class=\"token operator\">&#061;<\/span>cv<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u57fa\u5c3c\u7cfb\u6570 \u5e73\u5747\u51c6\u786e\u7387: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>scores_gini<span class=\"token punctuation\">.<\/span>mean<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\"> (&#043;\/- <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>scores_gini<span class=\"token punctuation\">.<\/span>std<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">)&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u4fe1\u606f\u71b5 \u5e73\u5747\u51c6\u786e\u7387: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>scores_entropy<span class=\"token punctuation\">.<\/span>mean<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\"> (&#043;\/- <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>scores_entropy<span class=\"token punctuation\">.<\/span>std<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">)&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\u901a\u5e38\u4e24\u8005\u5dee\u5f02\u4e0d\u5927&#xff0c;\u57fa\u5c3c\u7cfb\u6570\u7a0d\u5feb\u3002&#034;<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>\u5b9e\u62183&#xff1a;\u9884\u526a\u679d\u53c2\u6570\u5bf9\u8fc7\u62df\u5408\u7684\u5f71\u54cd<\/h4>\n<p><span class=\"token comment\"># -*- coding: utf-8 -*-<\/span><br \/>\n<span class=\"token triple-quoted-string string\">&#034;&#034;&#034;<br \/>\n\u6f14\u793a\u9884\u526a\u679d&#xff08;max_depth, min_samples_split&#xff09;\u5982\u4f55\u9632\u6b62\u8fc7\u62df\u5408<br \/>\n\u4f7f\u7528\u751f\u6210\u7684\u6570\u636e&#xff08;\u8bad\u7ec3\u96c6\u566a\u58f0\u5927&#xff09;<br \/>\n&#034;&#034;&#034;<\/span><\/p>\n<p><span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>datasets <span class=\"token keyword\">import<\/span> make_classification<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>model_selection <span class=\"token keyword\">import<\/span> train_test_split<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>tree <span class=\"token keyword\">import<\/span> DecisionTreeClassifier<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>metrics <span class=\"token keyword\">import<\/span> accuracy_score<br \/>\n<span class=\"token keyword\">import<\/span> matplotlib<span class=\"token punctuation\">.<\/span>pyplot <span class=\"token keyword\">as<\/span> plt<\/p>\n<p><span class=\"token comment\"># \u751f\u6210\u590d\u6742\u6570\u636e&#xff08;\u6709\u566a\u58f0&#xff09;<\/span><br \/>\nX<span class=\"token punctuation\">,<\/span> y <span class=\"token operator\">&#061;<\/span> make_classification<span class=\"token punctuation\">(<\/span>n_samples<span class=\"token operator\">&#061;<\/span><span class=\"token number\">500<\/span><span class=\"token punctuation\">,<\/span> n_features<span class=\"token operator\">&#061;<\/span><span class=\"token number\">10<\/span><span class=\"token punctuation\">,<\/span> n_informative<span class=\"token operator\">&#061;<\/span><span class=\"token number\">8<\/span><span class=\"token punctuation\">,<\/span><br \/>\n                           n_redundant<span class=\"token operator\">&#061;<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> flip_y<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.1<\/span><span class=\"token punctuation\">,<\/span> random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><br \/>\nX_train<span class=\"token punctuation\">,<\/span> X_test<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">,<\/span> y_test <span class=\"token operator\">&#061;<\/span> train_test_split<span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">,<\/span> test_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.2<\/span><span class=\"token punctuation\">,<\/span> random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u4e0d\u540c\u6df1\u5ea6<\/span><br \/>\ndepths <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">21<\/span><span class=\"token punctuation\">)<\/span><br \/>\ntrain_acc <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span><br \/>\ntest_acc <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span><\/p>\n<p><span class=\"token keyword\">for<\/span> depth <span class=\"token keyword\">in<\/span> depths<span class=\"token punctuation\">:<\/span><br \/>\n    dt <span class=\"token operator\">&#061;<\/span> DecisionTreeClassifier<span class=\"token punctuation\">(<\/span>max_depth<span class=\"token operator\">&#061;<\/span>depth<span class=\"token punctuation\">,<\/span> random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    dt<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">)<\/span><br \/>\n    train_acc<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>accuracy_score<span class=\"token punctuation\">(<\/span>y_train<span class=\"token punctuation\">,<\/span> dt<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    test_acc<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>accuracy_score<span class=\"token punctuation\">(<\/span>y_test<span class=\"token punctuation\">,<\/span> dt<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>X_test<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>plt<span class=\"token punctuation\">.<\/span>figure<span class=\"token punctuation\">(<\/span>figsize<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">10<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">6<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>plot<span class=\"token punctuation\">(<\/span>depths<span class=\"token punctuation\">,<\/span> train_acc<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;o-&#039;<\/span><span class=\"token punctuation\">,<\/span> label<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;\u8bad\u7ec3\u51c6\u786e\u7387&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>plot<span class=\"token punctuation\">(<\/span>depths<span class=\"token punctuation\">,<\/span> test_acc<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;s-&#039;<\/span><span class=\"token punctuation\">,<\/span> label<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;\u6d4b\u8bd5\u51c6\u786e\u7387&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>xlabel<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u6700\u5927\u6df1\u5ea6 max_depth&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>ylabel<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u51c6\u786e\u7387&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>title<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u51b3\u7b56\u6811\u6df1\u5ea6\u4e0e\u8fc7\u62df\u5408&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>legend<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>grid<span class=\"token punctuation\">(<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>show<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u6700\u4f73\u6df1\u5ea6: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>depths<span class=\"token punctuation\">[<\/span>test_acc<span class=\"token punctuation\">.<\/span>index<span class=\"token punctuation\">(<\/span><span class=\"token builtin\">max<\/span><span class=\"token punctuation\">(<\/span>test_acc<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>\u5b9e\u62184&#xff1a;\u7279\u5f81\u91cd\u8981\u6027\u5206\u6790<\/h4>\n<p><span class=\"token comment\"># -*- coding: utf-8 -*-<\/span><br \/>\n<span class=\"token triple-quoted-string string\">&#034;&#034;&#034;<br \/>\n\u63d0\u53d6\u51b3\u7b56\u6811\u7684\u7279\u5f81\u91cd\u8981\u6027&#xff0c;\u5e76\u53ef\u89c6\u5316<br \/>\n\u4f7f\u7528\u7ea2\u9152\u6570\u636e\u96c6<br \/>\n&#034;&#034;&#034;<\/span><\/p>\n<p><span class=\"token keyword\">import<\/span> numpy <span class=\"token keyword\">as<\/span> np<br \/>\n<span class=\"token keyword\">import<\/span> matplotlib<span class=\"token punctuation\">.<\/span>pyplot <span class=\"token keyword\">as<\/span> plt<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>datasets <span class=\"token keyword\">import<\/span> load_wine<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>tree <span class=\"token keyword\">import<\/span> DecisionTreeClassifier<\/p>\n<p>wine <span class=\"token operator\">&#061;<\/span> load_wine<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\nX<span class=\"token punctuation\">,<\/span> y <span class=\"token operator\">&#061;<\/span> wine<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">,<\/span> wine<span class=\"token punctuation\">.<\/span>target<br \/>\nfeature_names <span class=\"token operator\">&#061;<\/span> wine<span class=\"token punctuation\">.<\/span>feature_names<\/p>\n<p>dt <span class=\"token operator\">&#061;<\/span> DecisionTreeClassifier<span class=\"token punctuation\">(<\/span>random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><br \/>\ndt<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">)<\/span><\/p>\n<p>importances <span class=\"token operator\">&#061;<\/span> dt<span class=\"token punctuation\">.<\/span>feature_importances_<br \/>\nindices <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>argsort<span class=\"token punctuation\">(<\/span>importances<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">:<\/span><span class=\"token operator\">&#8211;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><\/p>\n<p>plt<span class=\"token punctuation\">.<\/span>figure<span class=\"token punctuation\">(<\/span>figsize<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">10<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">6<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>barh<span class=\"token punctuation\">(<\/span><span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span><span class=\"token builtin\">len<\/span><span class=\"token punctuation\">(<\/span>importances<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> importances<span class=\"token punctuation\">[<\/span>indices<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> align<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;center&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>yticks<span class=\"token punctuation\">(<\/span><span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span><span class=\"token builtin\">len<\/span><span class=\"token punctuation\">(<\/span>importances<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> np<span class=\"token punctuation\">.<\/span>array<span class=\"token punctuation\">(<\/span>feature_names<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">[<\/span>indices<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>xlabel<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u7279\u5f81\u91cd\u8981\u6027&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>title<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u51b3\u7b56\u6811\u7279\u5f81\u91cd\u8981\u6027&#xff08;\u7ea2\u9152\u6570\u636e\u96c6&#xff09;&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>gca<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>invert_yaxis<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>show<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\u7279\u5f81\u91cd\u8981\u6027\u6392\u5e8f:&#034;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">for<\/span> i <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span><span class=\"token builtin\">len<\/span><span class=\"token punctuation\">(<\/span>importances<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>i<span class=\"token operator\">&#043;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">. <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>feature_names<span class=\"token punctuation\">[<\/span>indices<span class=\"token punctuation\">[<\/span>i<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>importances<span class=\"token punctuation\">[<\/span>indices<span class=\"token punctuation\">[<\/span>i<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>\u5b9e\u62185&#xff1a;\u540e\u526a\u679d\u2014\u2014\u4ee3\u4ef7\u590d\u6742\u5ea6\u526a\u679d&#xff08;CCP&#xff09;<\/h4>\n<p><span class=\"token comment\"># -*- coding: utf-8 -*-<\/span><br \/>\n<span class=\"token triple-quoted-string string\">&#034;&#034;&#034;<br \/>\n\u4f7f\u7528 ccp_alpha \u8fdb\u884c\u4ee3\u4ef7\u590d\u6742\u5ea6\u540e\u526a\u679d<br \/>\n\u5bf9\u6bd4\u4e0d\u540c ccp_alpha \u7684\u6811\u590d\u6742\u5ea6\u4e0e\u51c6\u786e\u7387<br \/>\n&#034;&#034;&#034;<\/span><\/p>\n<p><span class=\"token keyword\">import<\/span> numpy <span class=\"token keyword\">as<\/span> np<br \/>\n<span class=\"token keyword\">import<\/span> matplotlib<span class=\"token punctuation\">.<\/span>pyplot <span class=\"token keyword\">as<\/span> plt<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>datasets <span class=\"token keyword\">import<\/span> make_classification<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>model_selection <span class=\"token keyword\">import<\/span> train_test_split<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>tree <span class=\"token keyword\">import<\/span> DecisionTreeClassifier<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>metrics <span class=\"token keyword\">import<\/span> accuracy_score<\/p>\n<p>X<span class=\"token punctuation\">,<\/span> y <span class=\"token operator\">&#061;<\/span> make_classification<span class=\"token punctuation\">(<\/span>n_samples<span class=\"token operator\">&#061;<\/span><span class=\"token number\">500<\/span><span class=\"token punctuation\">,<\/span> n_features<span class=\"token operator\">&#061;<\/span><span class=\"token number\">10<\/span><span class=\"token punctuation\">,<\/span> random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><br \/>\nX_train<span class=\"token punctuation\">,<\/span> X_test<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">,<\/span> y_test <span class=\"token operator\">&#061;<\/span> train_test_split<span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">,<\/span> test_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.2<\/span><span class=\"token punctuation\">,<\/span> random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u5148\u8bad\u7ec3\u4e00\u68f5\u5b8c\u5168\u751f\u957f\u7684\u6811&#xff08;\u4e0d\u526a\u679d&#xff09;<\/span><br \/>\ndt_full <span class=\"token operator\">&#061;<\/span> DecisionTreeClassifier<span class=\"token punctuation\">(<\/span>random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><br \/>\ndt_full<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u5b8c\u5168\u751f\u957f\u6811\u6df1\u5ea6: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>dt_full<span class=\"token punctuation\">.<\/span>tree_<span class=\"token punctuation\">.<\/span>max_depth<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">, \u53f6\u5b50\u6570: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>dt_full<span class=\"token punctuation\">.<\/span>tree_<span class=\"token punctuation\">.<\/span>n_leaves<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u8bad\u7ec3\u96c6\u51c6\u786e\u7387: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>accuracy_score<span class=\"token punctuation\">(<\/span>y_train<span class=\"token punctuation\">,<\/span> dt_full<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u6d4b\u8bd5\u96c6\u51c6\u786e\u7387: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>accuracy_score<span class=\"token punctuation\">(<\/span>y_test<span class=\"token punctuation\">,<\/span> dt_full<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>X_test<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u5c1d\u8bd5\u4e0d\u540c\u7684 ccp_alpha<\/span><br \/>\nalphas <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>linspace<span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.05<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">20<\/span><span class=\"token punctuation\">)<\/span><br \/>\ntrain_scores <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span><br \/>\ntest_scores <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span><br \/>\ndepths <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span><br \/>\nleaves <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span><\/p>\n<p><span class=\"token keyword\">for<\/span> alpha <span class=\"token keyword\">in<\/span> alphas<span class=\"token punctuation\">:<\/span><br \/>\n    dt <span class=\"token operator\">&#061;<\/span> DecisionTreeClassifier<span class=\"token punctuation\">(<\/span>ccp_alpha<span class=\"token operator\">&#061;<\/span>alpha<span class=\"token punctuation\">,<\/span> random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    dt<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">)<\/span><br \/>\n    depths<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>dt<span class=\"token punctuation\">.<\/span>tree_<span class=\"token punctuation\">.<\/span>max_depth<span class=\"token punctuation\">)<\/span><br \/>\n    leaves<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>dt<span class=\"token punctuation\">.<\/span>tree_<span class=\"token punctuation\">.<\/span>n_leaves<span class=\"token punctuation\">)<\/span><br \/>\n    train_scores<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>accuracy_score<span class=\"token punctuation\">(<\/span>y_train<span class=\"token punctuation\">,<\/span> dt<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    test_scores<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>accuracy_score<span class=\"token punctuation\">(<\/span>y_test<span class=\"token punctuation\">,<\/span> dt<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>X_test<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>best_idx <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>argmax<span class=\"token punctuation\">(<\/span>test_scores<span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\\\\n\u6700\u4f73 ccp_alpha &#061; <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>alphas<span class=\"token punctuation\">[<\/span>best_idx<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u5bf9\u5e94\u6df1\u5ea6: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>depths<span class=\"token punctuation\">[<\/span>best_idx<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">, \u53f6\u5b50\u8282\u70b9\u6570: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>leaves<span class=\"token punctuation\">[<\/span>best_idx<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u6d4b\u8bd5\u51c6\u786e\u7387: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>test_scores<span class=\"token punctuation\">[<\/span>best_idx<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u7ed8\u5236\u6027\u80fd\u968f ccp_alpha \u53d8\u5316\u66f2\u7ebf<\/span><br \/>\nfig<span class=\"token punctuation\">,<\/span> axes <span class=\"token operator\">&#061;<\/span> plt<span class=\"token punctuation\">.<\/span>subplots<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> figsize<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">12<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">5<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\naxes<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>plot<span class=\"token punctuation\">(<\/span>alphas<span class=\"token punctuation\">,<\/span> train_scores<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;o-&#039;<\/span><span class=\"token punctuation\">,<\/span> label<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;\u8bad\u7ec3\u51c6\u786e\u7387&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\naxes<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>plot<span class=\"token punctuation\">(<\/span>alphas<span class=\"token punctuation\">,<\/span> test_scores<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;s-&#039;<\/span><span class=\"token punctuation\">,<\/span> label<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;\u6d4b\u8bd5\u51c6\u786e\u7387&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\naxes<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>set_xlabel<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;ccp_alpha&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\naxes<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>set_ylabel<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u51c6\u786e\u7387&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\naxes<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>set_title<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u51c6\u786e\u7387 vs \u526a\u679d\u5f3a\u5ea6&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\naxes<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>legend<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\naxes<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>grid<span class=\"token punctuation\">(<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>axes<span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>plot<span class=\"token punctuation\">(<\/span>alphas<span class=\"token punctuation\">,<\/span> depths<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;o-&#039;<\/span><span class=\"token punctuation\">,<\/span> label<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;\u6811\u6df1\u5ea6&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\naxes<span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>plot<span class=\"token punctuation\">(<\/span>alphas<span class=\"token punctuation\">,<\/span> leaves<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;s-&#039;<\/span><span class=\"token punctuation\">,<\/span> label<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;\u53f6\u5b50\u8282\u70b9\u6570&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\naxes<span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>set_xlabel<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;ccp_alpha&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\naxes<span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>set_ylabel<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u590d\u6742\u5ea6&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\naxes<span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>set_title<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u6811\u590d\u6742\u5ea6 vs \u526a\u679d\u5f3a\u5ea6&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\naxes<span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>legend<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\naxes<span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>grid<span class=\"token punctuation\">(<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>tight_layout<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>show<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>\u5b9e\u62186&#xff1a;\u591a\u53c2\u6570\u7f51\u683c\u641c\u7d22&#xff08;\u9884\u526a\u679d &#043; \u540e\u526a\u679d&#xff09;<\/h4>\n<p><span class=\"token comment\"># -*- coding: utf-8 -*-<\/span><br \/>\n<span class=\"token triple-quoted-string string\">&#034;&#034;&#034;<br \/>\n\u4f7f\u7528 GridSearchCV \u8054\u5408\u8c03\u4f18\u51b3\u7b56\u6811\u7684\u9884\u526a\u679d\u53c2\u6570\u548c ccp_alpha<br \/>\n&#034;&#034;&#034;<\/span><\/p>\n<p><span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>datasets <span class=\"token keyword\">import<\/span> load_iris<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>model_selection <span class=\"token keyword\">import<\/span> GridSearchCV<span class=\"token punctuation\">,<\/span> train_test_split<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>tree <span class=\"token keyword\">import<\/span> DecisionTreeClassifier<\/p>\n<p>X<span class=\"token punctuation\">,<\/span> y <span class=\"token operator\">&#061;<\/span> load_iris<span class=\"token punctuation\">(<\/span>return_X_y<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><br \/>\nX_train<span class=\"token punctuation\">,<\/span> X_test<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">,<\/span> y_test <span class=\"token operator\">&#061;<\/span> train_test_split<span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">,<\/span> test_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.2<\/span><span class=\"token punctuation\">,<\/span> random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>param_grid <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n    <span class=\"token string\">&#039;max_depth&#039;<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">5<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">7<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token boolean\">None<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    <span class=\"token string\">&#039;min_samples_split&#039;<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">5<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">10<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    <span class=\"token string\">&#039;min_samples_leaf&#039;<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">4<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    <span class=\"token string\">&#039;ccp_alpha&#039;<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.005<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.01<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.02<\/span><span class=\"token punctuation\">]<\/span><br \/>\n<span class=\"token punctuation\">}<\/span><\/p>\n<p>dt <span class=\"token operator\">&#061;<\/span> DecisionTreeClassifier<span class=\"token punctuation\">(<\/span>random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><br \/>\ngrid <span class=\"token operator\">&#061;<\/span> GridSearchCV<span class=\"token punctuation\">(<\/span>dt<span class=\"token punctuation\">,<\/span> param_grid<span class=\"token punctuation\">,<\/span> cv<span class=\"token operator\">&#061;<\/span><span class=\"token number\">5<\/span><span class=\"token punctuation\">,<\/span> scoring<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;accuracy&#039;<\/span><span class=\"token punctuation\">,<\/span> n_jobs<span class=\"token operator\">&#061;<\/span><span class=\"token operator\">&#8211;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\ngrid<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\u6700\u4f73\u53c2\u6570:&#034;<\/span><span class=\"token punctuation\">,<\/span> grid<span class=\"token punctuation\">.<\/span>best_params_<span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\u6700\u4f73\u4ea4\u53c9\u9a8c\u8bc1\u51c6\u786e\u7387: {:.4f}&#034;<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">format<\/span><span class=\"token punctuation\">(<\/span>grid<span class=\"token punctuation\">.<\/span>best_score_<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\u6d4b\u8bd5\u96c6\u51c6\u786e\u7387: {:.4f}&#034;<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">format<\/span><span class=\"token punctuation\">(<\/span>grid<span class=\"token punctuation\">.<\/span>score<span class=\"token punctuation\">(<\/span>X_test<span class=\"token punctuation\">,<\/span> y_test<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>\u5b9e\u62187&#xff1a;\u51b3\u7b56\u6811\u56de\u5f52&#xff08;DecisionTreeRegressor&#xff09;<\/h4>\n<p><span class=\"token comment\"># -*- coding: utf-8 -*-<\/span><br \/>\n<span class=\"token triple-quoted-string string\">&#034;&#034;&#034;<br \/>\n\u51b3\u7b56\u6811\u56de\u5f52&#xff1a;\u62df\u5408\u5e26\u566a\u58f0\u7684\u6b63\u5f26\u66f2\u7ebf<br \/>\n\u5bf9\u6bd4\u4e0d\u540c\u6df1\u5ea6<br \/>\n&#034;&#034;&#034;<\/span><\/p>\n<p><span class=\"token keyword\">import<\/span> numpy <span class=\"token keyword\">as<\/span> np<br \/>\n<span class=\"token keyword\">import<\/span> matplotlib<span class=\"token punctuation\">.<\/span>pyplot <span class=\"token keyword\">as<\/span> plt<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>tree <span class=\"token keyword\">import<\/span> DecisionTreeRegressor<\/p>\n<p><span class=\"token comment\"># \u751f\u6210\u6570\u636e<\/span><br \/>\nnp<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>seed<span class=\"token punctuation\">(<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><br \/>\nX <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>sort<span class=\"token punctuation\">(<\/span><span class=\"token number\">5<\/span> <span class=\"token operator\">*<\/span> np<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>rand<span class=\"token punctuation\">(<\/span><span class=\"token number\">200<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> axis<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">)<\/span><br \/>\ny <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>sin<span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>ravel<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">&#043;<\/span> np<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>normal<span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.1<\/span><span class=\"token punctuation\">,<\/span> X<span class=\"token punctuation\">.<\/span>shape<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>X_train<span class=\"token punctuation\">,<\/span> X_test <span class=\"token operator\">&#061;<\/span> X<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token number\">150<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> X<span class=\"token punctuation\">[<\/span><span class=\"token number\">150<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">]<\/span><br \/>\ny_train<span class=\"token punctuation\">,<\/span> y_test <span class=\"token operator\">&#061;<\/span> y<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token number\">150<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">[<\/span><span class=\"token number\">150<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">]<\/span><\/p>\n<p>depths <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">5<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">20<\/span><span class=\"token punctuation\">]<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>figure<span class=\"token punctuation\">(<\/span>figsize<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">12<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">4<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">for<\/span> i<span class=\"token punctuation\">,<\/span> depth <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">enumerate<\/span><span class=\"token punctuation\">(<\/span>depths<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    dt_reg <span class=\"token operator\">&#061;<\/span> DecisionTreeRegressor<span class=\"token punctuation\">(<\/span>max_depth<span class=\"token operator\">&#061;<\/span>depth<span class=\"token punctuation\">,<\/span> random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    dt_reg<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">)<\/span><br \/>\n    y_pred <span class=\"token operator\">&#061;<\/span> dt_reg<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>X_test<span class=\"token punctuation\">)<\/span><br \/>\n    mse <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>mean<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">(<\/span>y_test <span class=\"token operator\">&#8211;<\/span> y_pred<span class=\"token punctuation\">)<\/span><span class=\"token operator\">**<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>    plt<span class=\"token punctuation\">.<\/span>subplot<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> i<span class=\"token operator\">&#043;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>scatter<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">,<\/span> alpha<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.5<\/span><span class=\"token punctuation\">,<\/span> label<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;\u8bad\u7ec3\u6570\u636e&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>scatter<span class=\"token punctuation\">(<\/span>X_test<span class=\"token punctuation\">,<\/span> y_test<span class=\"token punctuation\">,<\/span> alpha<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.5<\/span><span class=\"token punctuation\">,<\/span> marker<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;x&#039;<\/span><span class=\"token punctuation\">,<\/span> label<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;\u6d4b\u8bd5\u6570\u636e&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    X_plot <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>linspace<span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">5<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">300<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>reshape<span class=\"token punctuation\">(<\/span><span class=\"token operator\">&#8211;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    y_plot <span class=\"token operator\">&#061;<\/span> dt_reg<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>X_plot<span class=\"token punctuation\">)<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>plot<span class=\"token punctuation\">(<\/span>X_plot<span class=\"token punctuation\">,<\/span> y_plot<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;r-&#039;<\/span><span class=\"token punctuation\">,<\/span> label<span class=\"token operator\">&#061;<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#039;depth&#061;<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>depth<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">, MSE&#061;<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>mse<span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#039;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>xlabel<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;X&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>ylabel<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;y&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>legend<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>tight_layout<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>show<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h3>\u6848\u4f8b\u5b9e\u64cd\u6f14\u793a<\/h3>\n<h4>\u6848\u4f8b&#xff1a;\u4fe1\u7528\u5361\u8fdd\u7ea6\u9884\u6d4b&#xff08;\u51b3\u7b56\u6811\u4e8c\u5206\u7c7b&#xff09;<\/h4>\n<p><span class=\"token comment\"># \u6a21\u62df\u4fe1\u7528\u5361\u8fdd\u7ea6\u6570\u636e&#xff08;\u771f\u5b9e\u6848\u4f8b\u53ef\u66ff\u6362&#xff09;<\/span><br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>datasets <span class=\"token keyword\">import<\/span> make_classification<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>model_selection <span class=\"token keyword\">import<\/span> train_test_split<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>tree <span class=\"token keyword\">import<\/span> DecisionTreeClassifier<span class=\"token punctuation\">,<\/span> plot_tree<br \/>\n<span class=\"token keyword\">import<\/span> matplotlib<span class=\"token punctuation\">.<\/span>pyplot <span class=\"token keyword\">as<\/span> plt<\/p>\n<p><span class=\"token comment\"># \u751f\u6210\u6570\u636e&#xff08;\u7279\u5f81&#xff1a;\u6536\u5165\u3001\u5e74\u9f84\u3001\u8d1f\u503a\u6bd4\u3001\u4fe1\u7528\u8bb0\u5f55\u957f\u5ea6\u7b49&#xff09;<\/span><br \/>\nX<span class=\"token punctuation\">,<\/span> y <span class=\"token operator\">&#061;<\/span> make_classification<span class=\"token punctuation\">(<\/span>n_samples<span class=\"token operator\">&#061;<\/span><span class=\"token number\">2000<\/span><span class=\"token punctuation\">,<\/span> n_features<span class=\"token operator\">&#061;<\/span><span class=\"token number\">8<\/span><span class=\"token punctuation\">,<\/span> n_informative<span class=\"token operator\">&#061;<\/span><span class=\"token number\">6<\/span><span class=\"token punctuation\">,<\/span><br \/>\n                           n_redundant<span class=\"token operator\">&#061;<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> flip_y<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.05<\/span><span class=\"token punctuation\">,<\/span> random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><br \/>\nfeature_names <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;\u6536\u5165&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;\u5e74\u9f84&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;\u8d1f\u503a\u6bd4&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;\u4fe1\u7528\u8bb0\u5f55\u957f\u5ea6&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;\u4f7f\u7528\u7387&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;\u903e\u671f\u6b21\u6570&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;\u67e5\u8be2\u6b21\u6570&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;\u989d\u5ea6\u5229\u7528\u7387&#039;<\/span><span class=\"token punctuation\">]<\/span><br \/>\nX_train<span class=\"token punctuation\">,<\/span> X_test<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">,<\/span> y_test <span class=\"token operator\">&#061;<\/span> train_test_split<span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">,<\/span> test_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.2<\/span><span class=\"token punctuation\">,<\/span> random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>dt <span class=\"token operator\">&#061;<\/span> DecisionTreeClassifier<span class=\"token punctuation\">(<\/span>max_depth<span class=\"token operator\">&#061;<\/span><span class=\"token number\">5<\/span><span class=\"token punctuation\">,<\/span> min_samples_split<span class=\"token operator\">&#061;<\/span><span class=\"token number\">20<\/span><span class=\"token punctuation\">,<\/span> ccp_alpha<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.01<\/span><span class=\"token punctuation\">,<\/span> random_state<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><br \/>\ndt<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u8bad\u7ec3\u96c6\u51c6\u786e\u7387: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>dt<span class=\"token punctuation\">.<\/span>score<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u6d4b\u8bd5\u96c6\u51c6\u786e\u7387: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>dt<span class=\"token punctuation\">.<\/span>score<span class=\"token punctuation\">(<\/span>X_test<span class=\"token punctuation\">,<\/span> y_test<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u53ef\u89c6\u5316&#xff08;\u9650\u5236\u6df1\u5ea6\u540e\u6811\u8f83\u5c0f&#xff09;<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>figure<span class=\"token punctuation\">(<\/span>figsize<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">15<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">8<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplot_tree<span class=\"token punctuation\">(<\/span>dt<span class=\"token punctuation\">,<\/span> filled<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">,<\/span> feature_names<span class=\"token operator\">&#061;<\/span>feature_names<span class=\"token punctuation\">,<\/span> class_names<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;\u4e0d\u8fdd\u7ea6&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;\u8fdd\u7ea6&#039;<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> rounded<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>title<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;\u4fe1\u7528\u5361\u8fdd\u7ea6\u9884\u6d4b\u51b3\u7b56\u6811&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nplt<span class=\"token punctuation\">.<\/span>show<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h3>\u5e38\u89c1\u62a5\u9519\u4e0e\u907f\u5751\u6307\u5357<\/h3>\n<h4>\u62a5\u95191&#xff1a;ValueError: min_samples_split must be an integer<\/h4>\n<p>\u539f\u56e0&#xff1a;min_samples_split \u5e94\u8bbe\u7f6e\u4e3a\u6574\u6570&#xff08;\u6700\u5c0f\u6837\u672c\u6570&#xff09;&#xff0c;\u4e0d\u80fd\u662f\u5206\u6570\u3002<\/p>\n<p>\u89e3\u51b3&#xff1a;\u786e\u4fdd\u4f7f\u7528\u6574\u6570\u3002<\/p>\n<h4>\u62a5\u95192&#xff1a;\u6811\u592a\u5927&#xff0c;plot_tree \u663e\u793a\u6a21\u7cca<\/h4>\n<p>\u539f\u56e0&#xff1a;\u6811\u6df1\u5ea6\u5927\u3001\u53f6\u5b50\u591a&#xff0c;\u56fe\u5f62\u8d85\u51fa\u663e\u793a\u8303\u56f4\u3002<\/p>\n<p>\u89e3\u51b3&#xff1a;\u9650\u5236 max_depth \u6216\u4f7f\u7528 figsize \u8c03\u6574\u753b\u5e03\u5927\u5c0f&#xff0c;\u6216\u6539\u7528 export_graphviz \u8f93\u51fa\u5230\u6587\u4ef6\u3002<\/p>\n<h4>\u62a5\u95193&#xff1a;\u7279\u5f81\u91cd\u8981\u6027\u5168\u4e3a\u96f6\u6216\u67d0\u4e9b\u7279\u5f81\u91cd\u8981\u6027\u5f02\u5e38<\/h4>\n<p>\u539f\u56e0&#xff1a;\u6570\u636e\u4e2d\u5b58\u5728\u5927\u91cf\u65e0\u5173\u7279\u5f81&#xff0c;\u6216\u6811\u6df1\u5ea6\u592a\u6d45\u3002<\/p>\n<p>\u89e3\u51b3&#xff1a;\u68c0\u67e5\u6570\u636e&#xff0c;\u6216\u589e\u52a0\u6811\u590d\u6742\u5ea6\u3002<\/p>\n<h4>\u62a5\u95194&#xff1a;RuntimeError: Tree is too large \u5728\u53ef\u89c6\u5316\u65f6<\/h4>\n<p>\u89e3\u51b3&#xff1a;\u5148\u526a\u679d&#xff08;\u8bbe\u7f6e max_depth \u8f83\u5c0f&#xff0c;\u59823~5&#xff09;&#xff0c;\u6216\u5bfc\u51fa\u4e3a\u6587\u672c\u3002<\/p>\n<h4>\u907f\u5751\u603b\u7ed3<\/h4>\n<li>\u51b3\u7b56\u6811\u5bb9\u6613\u8fc7\u62df\u5408&#xff1a;\u52a1\u5fc5\u4f7f\u7528\u9884\u526a\u679d&#xff08;max_depth, min_samples_split&#xff09;\u6216\u540e\u526a\u679d&#xff08;ccp_alpha&#xff09;\u3002<\/li>\n<li>\u5bf9\u6570\u636e\u5fae\u5c0f\u53d8\u5316\u654f\u611f&#xff1a;\u53ef\u91c7\u7528\u96c6\u6210\u65b9\u6cd5&#xff08;\u968f\u673a\u68ee\u6797&#xff09;\u63d0\u9ad8\u7a33\u5b9a\u6027\u3002<\/li>\n<li>\u7c7b\u522b\u4e0d\u5e73\u8861&#xff1a;\u8bbe\u7f6e class_weight&#061;&#039;balanced&#039;\u3002<\/li>\n<li>\u7279\u5f81\u5c3a\u5ea6\u4e0d\u5f71\u54cd\u51b3\u7b56\u6811&#xff0c;\u4e0d\u9700\u8981\u6807\u51c6\u5316\u3002<\/li>\n<li>\u53ef\u89c6\u5316\u524d\u5148\u9650\u5236\u6df1\u5ea6&#xff0c;\u5426\u5219\u6811\u8fc7\u4e8e\u5e9e\u5927\u65e0\u6cd5\u9605\u8bfb\u3002<\/li>\n<h3>\u77e5\u8bc6\u70b9\u603b\u7ed3<\/h3>\n<p>\u672c\u8bfe\u7cfb\u7edf\u8bb2\u89e3\u4e86\u51b3\u7b56\u6811\u5206\u7c7b\u7b97\u6cd5\u7684\u6838\u5fc3\u539f\u7406\u4e0e\u5b9e\u8df5&#xff1a;<\/p>\n<h4>\u539f\u7406<\/h4>\n<li>\u6784\u5efa\u8fc7\u7a0b&#xff1a;\u9012\u5f52\u9009\u62e9\u6700\u4f18\u7279\u5f81\u5206\u88c2&#xff0c;\u76f4\u5230\u8282\u70b9\u7eaf\u6216\u6ee1\u8db3\u505c\u6b62\u6761\u4ef6\u3002<\/li>\n<li>\u5206\u88c2\u51c6\u5219&#xff1a;\u57fa\u5c3c\u7cfb\u6570&#xff08;gini&#xff09;\u548c\u4fe1\u606f\u71b5&#xff08;entropy&#xff09;&#xff0c;\u4e24\u8005\u90fd\u5ea6\u91cf\u8282\u70b9\u7684\u4e0d\u7eaf\u5ea6\u3002<\/li>\n<li>\u526a\u679d&#xff1a;\u9884\u526a\u679d&#xff08;\u9650\u5236\u6df1\u5ea6\u3001\u6700\u5c11\u6837\u672c\u6570&#xff09;\u548c\u540e\u526a\u679d&#xff08;\u4ee3\u4ef7\u590d\u6742\u5ea6\u526a\u679d ccp_alpha&#xff09;\u9632\u6b62\u8fc7\u62df\u5408\u3002<\/li>\n<h4>API<\/h4>\n<li>DecisionTreeClassifier \u6838\u5fc3\u53c2\u6570&#xff1a;criterion, max_depth, min_samples_split, min_samples_leaf, ccp_alpha, class_weight\u3002<\/li>\n<li>\u65b9\u6cd5&#xff1a;fit, predict, predict_proba, score\u3002<\/li>\n<li>\u5c5e\u6027&#xff1a;feature_importances_, tree_\u3002<\/li>\n<h4>\u53ef\u89c6\u5316\u4e0e\u89e3\u91ca<\/h4>\n<li>plot_tree \u76f4\u63a5\u7ed8\u5236\u51b3\u7b56\u6811\u3002<\/li>\n<li>feature_importances_ \u8f93\u51fa\u7279\u5f81\u91cd\u8981\u6027\u3002<\/li>\n<h4>\u6269\u5c55<\/h4>\n<li>DecisionTreeRegressor \u7528\u4e8e\u56de\u5f52\u4efb\u52a1&#xff0c;\u539f\u7406\u7c7b\u4f3c&#xff08;\u5206\u88c2\u51c6\u5219\u4e3a\u65b9\u5dee\u51cf\u5c11&#xff09;\u3002<\/li>\n<li>\u51b3\u7b56\u6811\u662f\u968f\u673a\u68ee\u6797\u3001\u68af\u5ea6\u63d0\u5347\u7b49\u96c6\u6210\u6a21\u578b\u7684\u57fa\u7840\u3002<\/li>\n<h3>\u8bfe\u540e\u7ec3\u4e60\u9898<\/h3>\n<h4>\u9009\u62e9\u9898<\/h4>\n<li>\n<p>\u51b3\u7b56\u6811\u5206\u88c2\u65f6&#xff0c;\u57fa\u5c3c\u7cfb\u6570\u4e3a0\u8868\u793a&#xff1a; A. \u8282\u70b9\u5305\u542b\u5355\u4e00\u7c7b\u522b B. \u8282\u70b9\u4e2d\u5404\u7c7b\u6bd4\u4f8b\u76f8\u7b49 C. \u8282\u70b9\u6837\u672c\u6570\u4e3a0 D. \u5206\u88c2\u589e\u76ca\u6700\u5927<\/p>\n<\/li>\n<li>\n<p>\u4ee5\u4e0b\u54ea\u4e2a\u53c2\u6570\u5c5e\u4e8e\u540e\u526a\u679d\u53c2\u6570&#xff1f; A. max_depth B. min_samples_split C. ccp_alpha D. min_samples_leaf<\/p>\n<\/li>\n<li>\n<p>\u51b3\u7b56\u6811\u5bf9\u7279\u5f81\u7f29\u653e\u7684\u6001\u5ea6\u662f&#xff1a; A. \u5fc5\u987b\u6807\u51c6\u5316 B. \u5fc5\u987b\u5f52\u4e00\u5316 C. \u4e0d\u9700\u8981 D. \u53d6\u51b3\u4e8e\u6570\u636e\u7c7b\u578b<\/p>\n<\/li>\n<h4>\u586b\u7a7a\u9898<\/h4>\n<p><span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>tree <span class=\"token keyword\">import<\/span> DecisionTreeClassifier<\/p>\n<p><span class=\"token comment\"># 1. \u521b\u5efa\u51b3\u7b56\u6811&#xff0c;\u4f7f\u7528\u71b5\u4f5c\u4e3a\u5206\u88c2\u51c6\u5219&#xff0c;\u6700\u5927\u6df1\u5ea65<\/span><br \/>\ndt <span class=\"token operator\">&#061;<\/span> DecisionTreeClassifier<span class=\"token punctuation\">(<\/span>criterion<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;________&#039;<\/span><span class=\"token punctuation\">,<\/span> max_depth<span class=\"token operator\">&#061;<\/span>________<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># 2. \u8bad\u7ec3<\/span><br \/>\ndt<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># 3. \u9884\u6d4b\u6982\u7387<\/span><br \/>\nproba <span class=\"token operator\">&#061;<\/span> dt<span class=\"token punctuation\">.<\/span>________<span class=\"token punctuation\">(<\/span>X_test<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># 4. \u83b7\u53d6\u7279\u5f81\u91cd\u8981\u6027<\/span><br \/>\nimportance <span class=\"token operator\">&#061;<\/span> dt<span class=\"token punctuation\">.<\/span>________<\/p>\n<h4>\u5b9e\u64cd\u9898<\/h4>\n<li>\n<p>\u57fa\u5c3c vs \u71b5&#xff1a;\u5728\u7ea2\u9152\u6570\u636e\u96c6\u4e0a&#xff0c;\u5206\u522b\u7528\u57fa\u5c3c\u548c\u4fe1\u606f\u71b5\u8bad\u7ec3\u51b3\u7b56\u6811&#xff08;\u4e0d\u526a\u679d&#xff09;&#xff0c;\u6bd4\u8f83\u4ea4\u53c9\u9a8c\u8bc1\u51c6\u786e\u7387\u3002\u540c\u65f6\u8ba1\u7b97\u4e24\u79cd\u6811\u7684\u6df1\u5ea6\u548c\u53f6\u5b50\u8282\u70b9\u6570&#xff0c;\u5206\u6790\u5dee\u5f02\u539f\u56e0\u3002<\/p>\n<\/li>\n<li>\n<p>\u9884\u526a\u679d\u8c03\u4f18&#xff1a;\u4f7f\u7528 GridSearchCV \u5728\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\u4e0a\u641c\u7d22 max_depth \u548c min_samples_split \u7684\u6700\u4f73\u7ec4\u5408&#xff0c;\u8f93\u51fa\u6700\u4f73\u53c2\u6570\u548c\u6d4b\u8bd5\u51c6\u786e\u7387\u3002<\/p>\n<\/li>\n<li>\n<p>\u7279\u5f81\u91cd\u8981\u6027\u5206\u6790&#xff1a;\u5728 load_breast_cancer 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\/>\n<p>\u4e0b\u4e00\u8bfe\u9884\u544a&#xff1a;\u7b2c22\u8bfe\u6211\u4eec\u5c06\u5b66\u4e60\u652f\u6301\u5411\u91cf\u673a&#xff08;SVM&#xff09;\u2014\u2014\u5f3a\u5927\u7684\u5206\u7c7b\u5668&#xff0c;\u901a\u8fc7\u6838\u6280\u5de7\u5904\u7406\u7ebf\u6027\u4e0d\u53ef\u5206\u6570\u636e\u3002\u4f60\u5c06\u638c\u63e1\u6838\u51fd\u6570\u9009\u62e9\u3001\u53c2\u6570\u8c03\u4f18\u7b49\u6838\u5fc3\u6280\u80fd\u3002\u656c\u8bf7\u671f\u5f85&#xff01;<\/p>\n<hr \/>\n<h2>&#x1f517;\u300a30\u8282\u8bfe scikit-learn \u4ece\u5165\u95e8\u5230\u7cbe\u901a\u300b\u7cfb\u5217\u8bfe\u7a0b\u5bfc\u822a<\/h2>\n<p>\u53bb\u8ba2\u9605<\/p>\n<p>\u7b2c\u4e00\u90e8\u5206&#xff1a;\u57fa\u7840\u5165\u95e8 &amp; \u73af\u5883\u51c6\u5907&#xff08;1-6 \u8bfe&#xff09; \u7b2c\u4e8c\u90e8\u5206&#xff1a;\u6570\u636e\u9884\u5904\u7406 &amp; \u6570\u636e\u96c6\u64cd\u4f5c&#xff08;7-12 \u8bfe&#xff09; \u7b2c\u4e09\u90e8\u5206&#xff1a;\u4f20\u7edf\u673a\u5668\u5b66\u4e60\u56de\u5f52\u7b97\u6cd5&#xff08;13-17 \u8bfe&#xff09; 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