{"id":258,"date":"2016-05-07T21:15:29","date_gmt":"2016-05-07T21:15:29","guid":{"rendered":"http:\/\/blogs.softwareclue.com\/?p=258"},"modified":"2016-05-07T21:15:29","modified_gmt":"2016-05-07T21:15:29","slug":"10-%e7%a7%8d%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e7%ae%97%e6%b3%95%e7%9a%84%e8%a6%81%e7%82%b9","status":"publish","type":"post","link":"http:\/\/blog.softwareclues.com\/zh\/10-%e7%a7%8d%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e7%ae%97%e6%b3%95%e7%9a%84%e8%a6%81%e7%82%b9","title":{"rendered":"10 \u79cd\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u7684\u8981\u70b9"},"content":{"rendered":"<p><em id=\"post-date\" class=\"rich_media_meta rich_media_meta_text\">2015-10-24<\/em> <em class=\"rich_media_meta rich_media_meta_text\">\u4f2f\u4e50\u5728\u7ebf<\/em> <span class=\"rich_media_meta rich_media_meta_text rich_media_meta_nickname\">\u7a0b\u5e8f\u5458\u7684\u90a3\u4e9b\u4e8b<\/span><\/p>\n<p><strong>\u524d\u8a00<\/strong><\/p>\n<blockquote><p>\u8c37\u6b4c\u8463\u4e8b\u957f\u65bd\u5bc6\u7279\u66fe\u8bf4\u8fc7\uff1a\u867d\u7136\u8c37\u6b4c\u7684\u65e0\u4eba\u9a7e\u9a76\u6c7d\u8f66\u548c\u673a\u5668\u4eba\u53d7\u5230\u4e86\u8bb8\u591a\u5a92\u4f53\u5173\u6ce8\uff0c\u4f46\u662f\u8fd9\u5bb6\u516c\u53f8\u771f\u6b63\u7684\u672a\u6765\u5728\u4e8e\u673a\u5668\u5b66\u4e60\uff0c\u4e00\u79cd\u8ba9\u8ba1\u7b97\u673a\u66f4\u806a\u660e\u3001\u66f4\u4e2a\u6027\u5316\u7684\u6280\u672f\u3002<\/p><\/blockquote>\n<p>\u4e5f\u8bb8\u6211\u4eec\u751f\u6d3b\u5728\u4eba\u7c7b\u5386\u53f2\u4e0a\u6700\u5173\u952e\u7684\u65f6\u671f\uff1a\u4ece\u4f7f\u7528\u5927\u578b\u8ba1\u7b97\u673a\uff0c\u5230\u4e2a\u4eba\u7535\u8111\uff0c\u518d\u5230\u73b0\u5728\u7684\u4e91\u8ba1\u7b97\u3002\u5173\u952e\u7684\u4e0d\u662f\u8fc7\u53bb\u53d1\u751f\u4e86\u4ec0\u4e48\uff0c\u800c\u662f\u5c06\u6765\u4f1a\u6709\u4ec0\u4e48\u53d1\u751f\u3002<\/p>\n<p>\u5de5\u5177\u548c\u6280\u672f\u7684\u6c11\u4e3b\u5316\uff0c\u8ba9\u50cf\u6211\u8fd9\u6837\u7684\u4eba\u5bf9\u8fd9\u4e2a\u65f6\u671f\u5174\u594b\u4e0d\u5df2\u3002\u8ba1\u7b97\u7684\u84ec\u52c3\u53d1\u5c55\u4e5f\u662f\u4e00\u6837\u3002\u5982\u4eca\uff0c\u4f5c\u4e3a\u4e00\u540d\u6570\u636e\u79d1\u5b66\u5bb6\uff0c\u7528\u590d\u6742\u7684\u7b97\u6cd5\u5efa\u7acb\u6570\u636e\u5904\u7406\u673a\u5668\u4e00\u5c0f\u65f6\u80fd\u8d5a\u5230\u597d\u51e0\u7f8e\u91d1\u3002\u4f46\u80fd\u505a\u5230\u8fd9\u4e2a\u7a0b\u5ea6\u53ef\u5e76\u4e0d\u7b80\u5355\uff01\u6211\u4e5f\u66fe\u6709\u8fc7\u65e0\u6570\u9ed1\u6697\u7684\u65e5\u65e5\u591c\u591c\u3002<\/p>\n<p><strong>\u8c01\u80fd\u4ece\u8fd9\u7bc7\u6307\u5357\u91cc\u53d7\u76ca\u6700\u591a\uff1f<\/strong><\/p>\n<p><strong>\u6211\u4eca\u5929\u6240\u7ed9\u51fa\u7684\uff0c\u4e5f\u8bb8\u662f\u6211\u8fd9\u8f88\u5b50\u5199\u4e0b\u7684\u6700\u6709\u4ef7\u503c\u7684\u6307\u5357\u3002<\/strong><\/p>\n<p>\u8fd9\u7bc7\u6307\u5357\u7684\u76ee\u7684\uff0c\u662f\u4e3a\u90a3\u4e9b\u6709\u8ffd\u6c42\u7684\u6570\u636e\u79d1\u5b66\u5bb6\u548c\u673a\u5668\u5b66\u4e60\u72c2\u70ed\u8005\u4eec\uff0c\u7b80\u5316\u5b66\u4e60\u65c5\u9014\u3002\u8fd9\u7bc7\u6307\u5357\u4f1a\u8ba9\u4f60\u52a8\u624b\u89e3\u51b3\u673a\u5668\u5b66\u4e60\u7684\u95ee\u9898\uff0c\u5e76\u4ece\u5b9e\u8df5\u4e2d\u83b7\u5f97\u771f\u77e5\u3002\u6211\u63d0\u4f9b\u7684\u662f\u51e0\u4e2a\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u7684\u9ad8\u6c34\u5e73\u7406\u89e3\uff0c\u4ee5\u53ca\u8fd0\u884c\u8fd9\u4e9b\u7b97\u6cd5\u7684 R \u548c Python \u4ee3\u7801\u3002\u8fd9\u4e9b\u5e94\u8be5\u8db3\u4ee5\u8ba9\u4f60\u4eb2\u81ea\u8bd5\u4e00\u8bd5\u4e86\u3002<\/p>\n<p><img decoding=\"async\" class=\"aligncenter\" 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data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/fhujzoQe7TrlSXGhVbPSbH11K9Q6JTGeNBFv0wXtibS2Ig2pJvpYx0DAwrkUns1z9JvNI9WIbyQU7tWkaoyqHUQ\/0?wx_fmt=jpeg\" data-type=\"jpeg\" data-ratio=\"0.6828282828282828\" data-w=\"495\" \/><\/p>\n<p>\u6211\u7279\u5730\u8df3\u8fc7\u4e86\u8fd9\u4e9b\u6280\u672f\u80cc\u540e\u7684\u6570\u636e\uff0c\u56e0\u4e3a\u4e00\u5f00\u59cb\u4f60\u5e76\u4e0d\u9700\u8981\u7406\u89e3\u8fd9\u4e9b\u3002\u5982\u679c\u4f60\u60f3\u4ece\u6570\u636e\u5c42\u9762\u4e0a\u7406\u89e3\u8fd9\u4e9b\u7b97\u6cd5\uff0c\u4f60\u5e94\u8be5\u53bb\u522b\u5904\u627e\u627e\u3002\u4f46\u5982\u679c\u4f60\u60f3\u8981\u5728\u5f00\u59cb\u4e00\u4e2a\u673a\u5668\u5b66\u4e60\u9879\u76ee\u4e4b\u524d\u505a\u4e9b\u51c6\u5907\uff0c\u4f60\u4f1a\u559c\u6b22\u8fd9\u7bc7\u6587\u7ae0\u7684\u3002<\/p>\n<p><strong>\u5e7f\u4e49\u6765\u8bf4\uff0c\u6709\u4e09\u79cd\u673a\u5668\u5b66\u4e60\u7b97\u6cd5<\/strong><\/p>\n<p><strong>1\u3001 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\u5f3a\u5316\u5b66\u4e60\u7684\u4f8b\u5b50\u6709\u9a6c\u5c14\u53ef\u592b\u51b3\u7b56\u8fc7\u7a0b\u3002<\/p>\n<p><strong>\u5e38\u89c1\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u540d\u5355<\/strong><\/p>\n<p>\u8fd9\u91cc\u662f\u4e00\u4e2a\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u540d\u5355\u3002\u8fd9\u4e9b\u7b97\u6cd5\u51e0\u4e4e\u53ef\u4ee5\u7528\u5728\u6240\u6709\u7684\u6570\u636e\u95ee\u9898\u4e0a\uff1a<\/p>\n<blockquote>\n<ul class=\" list-paddingleft-2\">\n<li>\u7ebf\u6027\u56de\u5f52<\/li>\n<li>\u903b\u8f91\u56de\u5f52<\/li>\n<li>\u51b3\u7b56\u6811<\/li>\n<li>SVM<\/li>\n<li>\u6734\u7d20\u8d1d\u53f6\u65af<\/li>\n<li>K\u6700\u8fd1\u90bb\u7b97\u6cd5<\/li>\n<li>K\u5747\u503c\u7b97\u6cd5<\/li>\n<li>\u968f\u673a\u68ee\u6797\u7b97\u6cd5<\/li>\n<li>\u964d\u7ef4\u7b97\u6cd5<\/li>\n<li>Gradient Boost \u548c Adaboost \u7b97\u6cd5<\/li>\n<\/ul>\n<\/blockquote>\n<p><strong>1\u3001\u7ebf\u6027\u56de\u5f52<\/strong><\/p>\n<p>\u7ebf\u6027\u56de\u5f52\u901a\u5e38\u7528\u4e8e\u6839\u636e\u8fde\u7eed\u53d8\u91cf\u4f30\u8ba1\u5b9e\u9645\u6570\u503c\uff08\u623f\u4ef7\u3001\u547c\u53eb\u6b21\u6570\u3001\u603b\u9500\u552e\u989d\u7b49\uff09\u3002\u6211\u4eec\u901a\u8fc7\u62df\u5408\u6700\u4f73\u76f4\u7ebf\u6765\u5efa\u7acb\u81ea\u53d8\u91cf\u548c\u56e0\u53d8\u91cf\u7684\u5173\u7cfb\u3002\u8fd9\u6761\u6700\u4f73\u76f4\u7ebf\u53eb\u505a\u56de\u5f52\u7ebf\uff0c\u5e76\u4e14\u7528 Y= a *X + b \u8fd9\u6761\u7ebf\u6027\u7b49\u5f0f\u6765\u8868\u793a\u3002<\/p>\n<p>\u7406\u89e3\u7ebf\u6027\u56de\u5f52\u7684\u6700\u597d\u529e\u6cd5\u662f\u56de\u987e\u4e00\u4e0b\u7ae5\u5e74\u3002\u5047\u8bbe\u5728\u4e0d\u95ee\u5bf9\u65b9\u4f53\u91cd\u7684\u60c5\u51b5\u4e0b\uff0c\u8ba9\u4e00\u4e2a\u4e94\u5e74\u7ea7\u7684\u5b69\u5b50\u6309\u4f53\u91cd\u4ece\u8f7b\u5230\u91cd\u7684\u987a\u5e8f\u5bf9\u73ed\u4e0a\u7684\u540c\u5b66\u6392\u5e8f\uff0c\u4f60\u89c9\u5f97\u8fd9\u4e2a\u5b69\u5b50\u4f1a\u600e\u4e48\u505a\uff1f\u4ed6\uff08\u5979\uff09\u5f88\u53ef\u80fd\u4f1a\u76ee\u6d4b\u4eba\u4eec\u7684\u8eab\u9ad8\u548c\u4f53\u578b\uff0c\u7efc\u5408\u8fd9\u4e9b\u53ef\u89c1\u7684\u53c2\u6570\u6765\u6392\u5217\u4ed6\u4eec\u3002\u8fd9\u662f\u73b0\u5b9e\u751f\u6d3b\u4e2d\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u7684\u4f8b\u5b50\u3002\u5b9e\u9645\u4e0a\uff0c\u8fd9\u4e2a\u5b69\u5b50\u53d1\u73b0\u4e86\u8eab\u9ad8\u548c\u4f53\u578b\u4e0e\u4f53\u91cd\u6709\u4e00\u5b9a\u7684\u5173\u7cfb\uff0c\u8fd9\u4e2a\u5173\u7cfb\u770b\u8d77\u6765\u5f88\u50cf\u4e0a\u9762\u7684\u7b49\u5f0f\u3002<\/p>\n<p>\u5728\u8fd9\u4e2a\u7b49\u5f0f\u4e2d\uff1a<\/p>\n<blockquote>\n<ul class=\" list-paddingleft-2\">\n<li>Y\uff1a\u56e0\u53d8\u91cf<\/li>\n<li>a\uff1a\u659c\u7387<\/li>\n<li>x\uff1a\u81ea\u53d8\u91cf<\/li>\n<li>b \uff1a\u622a\u8ddd<\/li>\n<\/ul>\n<\/blockquote>\n<p>\u7cfb\u6570 a \u548c b \u53ef\u4ee5\u901a\u8fc7\u6700\u5c0f\u4e8c\u4e58\u6cd5\u83b7\u5f97\u3002<\/p>\n<p>\u53c2\u89c1\u4e0b\u4f8b\u3002\u6211\u4eec\u627e\u51fa\u6700\u4f73\u62df\u5408\u76f4\u7ebf y=0.2811x+13.9\u3002\u5df2\u77e5\u4eba\u7684\u8eab\u9ad8\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8fd9\u6761\u7b49\u5f0f\u6c42\u51fa\u4f53\u91cd\u3002<\/p>\n<p><img decoding=\"async\" src=\"data:image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAIAAACQd1PeAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAyBpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADw\/eHBhY2tldCBiZWdpbj0i77u\/IiBpZD0iVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkIj8+IDx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IkFkb2JlIFhNUCBDb3JlIDUuMC1jMDYwIDYxLjEzNDc3NywgMjAxMC8wMi8xMi0xNzozMjowMCAgICAgICAgIj4gPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4gPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIgeG1sbnM6eG1wPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvIiB4bWxuczp4bXBNTT0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wL21tLyIgeG1sbnM6c3RSZWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9zVHlwZS9SZXNvdXJjZVJlZiMiIHhtcDpDcmVhdG9yVG9vbD0iQWRvYmUgUGhvdG9zaG9wIENTNSBXaW5kb3dzIiB4bXBNTTpJbnN0YW5jZUlEPSJ4bXAuaWlkOkJDQzA1MTVGNkE2MjExRTRBRjEzODVCM0Q0NEVFMjFBIiB4bXBNTTpEb2N1bWVudElEPSJ4bXAuZGlkOkJDQzA1MTYwNkE2MjExRTRBRjEzODVCM0Q0NEVFMjFBIj4gPHhtcE1NOkRlcml2ZWRGcm9tIHN0UmVmOmluc3RhbmNlSUQ9InhtcC5paWQ6QkNDMDUxNUQ2QTYyMTFFNEFGMTM4NUIzRDQ0RUUyMUEiIHN0UmVmOmRvY3VtZW50SUQ9InhtcC5kaWQ6QkNDMDUxNUU2QTYyMTFFNEFGMTM4NUIzRDQ0RUUyMUEiLz4gPC9yZGY6RGVzY3JpcHRpb24+IDwvcmRmOlJERj4gPC94OnhtcG1ldGE+IDw\/eHBhY2tldCBlbmQ9InIiPz6p+a6fAAAAD0lEQVR42mJ89\/Y1QIABAAWXAsgVS\/hWAAAAAElFTkSuQmCC\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/fhujzoQe7TrlSXGhVbPSbH11K9Q6JTGeTx6wtyhSLCh0Kz8FppRuv97OsGXzSzLYicJBsJVnQDQbSTKRaOoicR2Q\/0?wx_fmt=jpeg\" data-type=\"jpeg\" data-ratio=\"0.5945378151260504\" data-w=\"476\" \/><\/p>\n<p>\u7ebf\u6027\u56de\u5f52\u7684\u4e24\u79cd\u4e3b\u8981\u7c7b\u578b\u662f\u4e00\u5143\u7ebf\u6027\u56de\u5f52\u548c\u591a\u5143\u7ebf\u6027\u56de\u5f52\u3002\u4e00\u5143\u7ebf\u6027\u56de\u5f52\u7684\u7279\u70b9\u662f\u53ea\u6709\u4e00\u4e2a\u81ea\u53d8\u91cf\u3002\u591a\u5143\u7ebf\u6027\u56de\u5f52\u7684\u7279\u70b9\u6b63\u5982\u5176\u540d\uff0c\u5b58\u5728\u591a\u4e2a\u81ea\u53d8\u91cf\u3002\u627e\u6700\u4f73\u62df\u5408\u76f4\u7ebf\u7684\u65f6\u5019\uff0c\u4f60\u53ef\u4ee5\u62df\u5408\u5230\u591a\u9879\u6216\u8005\u66f2\u7ebf\u56de\u5f52\u3002\u8fd9\u4e9b\u5c31\u88ab\u53eb\u505a\u591a\u9879\u6216\u66f2\u7ebf\u56de\u5f52\u3002<\/p>\n<p><strong>Python \u4ee3\u7801<\/strong><\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"python comments\">#Import Library<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"python comments\">#Import other necessary libraries like pandas, numpy...<\/code><\/div>\n<div class=\"line number3 index2 alt2\"><code class=\"python keyword\">from<\/code> <code class=\"python plain\">sklearn <\/code><code class=\"python keyword\">import<\/code> <code class=\"python plain\">linear_model<\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"python comments\">#Load Train and Test datasets<\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"python comments\">#Identify feature and response variable(s) and values must be numeric and numpy arrays<\/code><\/div>\n<div class=\"line number7 index6 alt2\"><code class=\"python plain\">x_train<\/code><code class=\"python keyword\">=<\/code><code class=\"python plain\">input_variables_values_training_datasets<\/code><\/div>\n<div class=\"line number8 index7 alt1\"><code class=\"python plain\">y_train<\/code><code class=\"python keyword\">=<\/code><code class=\"python plain\">target_variables_values_training_datasets<\/code><\/div>\n<div class=\"line number9 index8 alt2\"><code class=\"python plain\">x_test<\/code><code class=\"python keyword\">=<\/code><code class=\"python plain\">input_variables_values_test_datasets<\/code><\/div>\n<div class=\"line number11 index10 alt2\"><code class=\"python comments\"># Create linear regression object<\/code><\/div>\n<div class=\"line number12 index11 alt1\"><code class=\"python plain\">linear <\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">linear_model.LinearRegression()<\/code><\/div>\n<div class=\"line number14 index13 alt1\"><code class=\"python comments\"># Train the model using the training sets and check score<\/code><\/div>\n<div class=\"line number15 index14 alt2\"><code class=\"python plain\">linear.fit(x_train, y_train)<\/code><\/div>\n<div class=\"line number16 index15 alt1\"><code class=\"python plain\">linear.score(x_train, y_train)<\/code><\/div>\n<div class=\"line number18 index17 alt1\"><code class=\"python comments\">#Equation coefficient and Intercept<\/code><\/div>\n<div class=\"line number19 index18 alt2\"><code class=\"python functions\">print<\/code><code class=\"python plain\">(<\/code><code class=\"python string\">'Coefficient: n'<\/code><code class=\"python plain\">, linear.coef_)<\/code><\/div>\n<div class=\"line number20 index19 alt1\"><code class=\"python functions\">print<\/code><code class=\"python plain\">(<\/code><code class=\"python string\">'Intercept: n'<\/code><code class=\"python plain\">, linear.intercept_)<\/code><\/div>\n<div class=\"line number22 index21 alt1\"><code class=\"python comments\">#Predict Output<\/code><\/div>\n<div class=\"line number23 index22 alt2\"><code class=\"python plain\">predicted<\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">linear.predict(x_test)<\/code><\/div>\n<\/blockquote>\n<p><strong>R\u4ee3\u7801<\/strong><\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"text plain\">#Load Train and Test datasets<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"text plain\">#Identify feature and response variable(s) and values must be numeric and numpy arrays<\/code><\/div>\n<div class=\"line number3 index2 alt2\"><code class=\"text plain\">x_train &lt;- input_variables_values_training_datasets<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"text plain\">y_train &lt;- target_variables_values_training_datasets<\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"text plain\">x_test &lt;- input_variables_values_test_datasets<\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"text plain\">x &lt;- cbind(x_train,y_train)<\/code><\/div>\n<div class=\"line number8 index7 alt1\"><code class=\"text plain\"># Train the model using the training sets and check score<\/code><\/div>\n<div class=\"line number9 index8 alt2\"><code class=\"text plain\">linear &lt;- lm(y_train ~ ., data = x)<\/code><\/div>\n<div class=\"line number10 index9 alt1\"><code class=\"text plain\">summary(linear)<\/code><\/div>\n<div class=\"line number12 index11 alt1\"><code class=\"text plain\">#Predict Output<\/code><\/div>\n<div class=\"line number13 index12 alt2\"><code class=\"text plain\">predicted= predict(linear,x_test)<\/code><\/div>\n<\/blockquote>\n<p><strong>2\u3001\u903b\u8f91\u56de\u5f52<\/strong><\/p>\n<p>\u522b\u88ab\u5b83\u7684\u540d\u5b57\u8ff7\u60d1\u4e86\uff01\u8fd9\u662f\u4e00\u4e2a\u5206\u7c7b\u7b97\u6cd5\u800c\u4e0d\u662f\u4e00\u4e2a\u56de\u5f52\u7b97\u6cd5\u3002\u8be5\u7b97\u6cd5\u53ef\u6839\u636e\u5df2\u77e5\u7684\u4e00\u7cfb\u5217\u56e0\u53d8\u91cf\u4f30\u8ba1\u79bb\u6563\u6570\u503c\uff08\u6bd4\u65b9\u8bf4\u4e8c\u8fdb\u5236\u6570\u503c 0 \u6216 1 \uff0c\u662f\u6216\u5426\uff0c\u771f\u6216\u5047\uff09\u3002\u7b80\u5355\u6765\u8bf4\uff0c\u5b83\u901a\u8fc7\u5c06\u6570\u636e\u62df\u5408\u8fdb\u4e00\u4e2a\u903b\u8f91\u51fd\u6570\u6765\u9884\u4f30\u4e00\u4e2a\u4e8b\u4ef6\u51fa\u73b0\u7684\u6982\u7387\u3002\u56e0\u6b64\uff0c\u5b83\u4e5f\u88ab\u53eb\u505a\u903b\u8f91\u56de\u5f52\u3002\u56e0\u4e3a\u5b83\u9884\u4f30\u7684\u662f\u6982\u7387\uff0c\u6240\u4ee5\u5b83\u7684\u8f93\u51fa\u503c\u5927\u5c0f\u5728 0 \u548c 1 \u4e4b\u95f4\uff08\u6b63\u5982\u6240\u9884\u8ba1\u7684\u4e00\u6837\uff09\u3002<\/p>\n<p>\u8ba9\u6211\u4eec\u518d\u6b21\u901a\u8fc7\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\u6765\u7406\u89e3\u8fd9\u4e2a\u7b97\u6cd5\u3002<\/p>\n<p>\u5047\u8bbe\u4f60\u7684\u670b\u53cb\u8ba9\u4f60\u89e3\u5f00\u4e00\u4e2a\u8c1c\u9898\u3002\u8fd9\u53ea\u4f1a\u6709\u4e24\u4e2a\u7ed3\u679c\uff1a\u4f60\u89e3\u5f00\u4e86\u6216\u662f\u4f60\u6ca1\u6709\u89e3\u5f00\u3002\u60f3\u8c61\u4f60\u8981\u89e3\u7b54\u5f88\u591a\u9053\u9898\u6765\u627e\u51fa\u4f60\u6240\u64c5\u957f\u7684\u4e3b\u9898\u3002\u8fd9\u4e2a\u7814\u7a76\u7684\u7ed3\u679c\u5c31\u4f1a\u50cf\u662f\u8fd9\u6837\uff1a\u5047\u8bbe\u9898\u76ee\u662f\u4e00\u9053\u5341\u5e74\u7ea7\u7684\u4e09\u89d2\u51fd\u6570\u9898\uff0c\u4f60\u6709 70%\u7684\u53ef\u80fd\u4f1a\u89e3\u5f00\u8fd9\u9053\u9898\u3002\u7136\u800c\uff0c\u82e5\u9898\u76ee\u662f\u4e2a\u4e94\u5e74\u7ea7\u7684\u5386\u53f2\u9898\uff0c\u4f60\u53ea\u670930%\u7684\u53ef\u80fd\u6027\u56de\u7b54\u6b63\u786e\u3002\u8fd9\u5c31\u662f\u903b\u8f91\u56de\u5f52\u80fd\u63d0\u4f9b\u7ed9\u4f60\u7684\u4fe1\u606f\u3002<\/p>\n<p>\u4ece\u6570\u5b66\u4e0a\u770b\uff0c\u5728\u7ed3\u679c\u4e2d\uff0c\u51e0\u7387\u7684\u5bf9\u6570\u4f7f\u7528\u7684\u662f\u9884\u6d4b\u53d8\u91cf\u7684\u7ebf\u6027\u7ec4\u5408\u6a21\u578b\u3002<\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"text plain\">odds= p\/ (1-p) = probability of event occurrence \/ probability of not event occurrence<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"text plain\">ln(odds) = ln(p\/(1-p))<\/code><\/div>\n<div class=\"line number3 index2 alt2\"><code class=\"text plain\">logit(p) = ln(p\/(1-p)) = b0+b1X1+b2X2+b3X3....+bkXk<\/code><\/div>\n<\/blockquote>\n<p>\u5728\u4e0a\u9762\u7684\u5f0f\u5b50\u91cc\uff0cp \u662f\u6211\u4eec\u611f\u5174\u8da3\u7684\u7279\u5f81\u51fa\u73b0\u7684\u6982\u7387\u3002\u5b83\u9009\u7528\u4f7f\u89c2\u5bdf\u6837\u672c\u503c\u7684\u53ef\u80fd\u6027\u6700\u5927\u5316\u7684\u503c\u4f5c\u4e3a\u53c2\u6570\uff0c\u800c\u4e0d\u662f\u901a\u8fc7\u8ba1\u7b97\u8bef\u5dee\u5e73\u65b9\u548c\u7684\u6700\u5c0f\u503c\uff08\u5c31\u5982\u4e00\u822c\u7684\u56de\u5f52\u5206\u6790\u7528\u5230\u7684\u4e00\u6837\uff09\u3002<\/p>\n<p>\u73b0\u5728\u4f60\u4e5f\u8bb8\u8981\u95ee\u4e86\uff0c\u4e3a\u4ec0\u4e48\u6211\u4eec\u8981\u6c42\u51fa\u5bf9\u6570\u5462\uff1f\u7b80\u800c\u8a00\u4e4b\uff0c\u8fd9\u79cd\u65b9\u6cd5\u662f\u590d\u5236\u4e00\u4e2a\u9636\u68af\u51fd\u6570\u7684\u6700\u4f73\u65b9\u6cd5\u4e4b\u4e00\u3002\u6211\u672c\u53ef\u4ee5\u66f4\u8be6\u7ec6\u5730\u8bb2\u8ff0\uff0c\u4f46\u90a3\u5c31\u8fdd\u80cc\u672c\u7bc7\u6307\u5357\u7684\u4e3b\u65e8\u4e86\u3002<\/p>\n<p><img decoding=\"async\" src=\"data:image\/png;base64,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\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/fhujzoQe7TrlSXGhVbPSbH11K9Q6JTGebriaibrwzTpRCadeBlVSRwy5XcMQB5LXLufiatBelzD1dXxXGibmQvIdww\/0?wx_fmt=jpeg\" data-type=\"jpeg\" data-ratio=\"0.7608695652173914\" data-w=\"\" \/><\/p>\n<p><strong>Python\u4ee3\u7801<\/strong><\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"python comments\">#Import Library<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"python keyword\">from<\/code> <code class=\"python plain\">sklearn.linear_model <\/code><code class=\"python keyword\">import<\/code> <code class=\"python plain\">LogisticRegression<\/code><\/div>\n<div class=\"line number3 index2 alt2\"><code class=\"python comments\">#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"python comments\"># Create logistic regression object<\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"python plain\">model <\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">LogisticRegression()<\/code><\/div>\n<div class=\"line number7 index6 alt2\"><code class=\"python comments\"># Train the model using the training sets and check score<\/code><\/div>\n<div class=\"line number8 index7 alt1\"><code class=\"python plain\">model.fit(X, y)<\/code><\/div>\n<div class=\"line number9 index8 alt2\"><code class=\"python plain\">model.score(X, y)<\/code><\/div>\n<div class=\"line number11 index10 alt2\"><code class=\"python comments\">#Equation coefficient and Intercept<\/code><\/div>\n<div class=\"line number12 index11 alt1\"><code class=\"python functions\">print<\/code><code class=\"python plain\">(<\/code><code class=\"python string\">'Coefficient: n'<\/code><code class=\"python plain\">, model.coef_)<\/code><\/div>\n<div class=\"line number13 index12 alt2\"><code class=\"python functions\">print<\/code><code class=\"python plain\">(<\/code><code class=\"python string\">'Intercept: n'<\/code><code class=\"python plain\">, model.intercept_)<\/code><\/div>\n<div class=\"line number15 index14 alt2\"><code class=\"python comments\">#Predict Output<\/code><\/div>\n<div class=\"line number16 index15 alt1\"><code class=\"python plain\">predicted<\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">model.predict(x_test)<\/code><\/div>\n<\/blockquote>\n<p><strong>R\u4ee3\u7801<\/strong><\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"text plain\">x &lt;- cbind(x_train,y_train)<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"text plain\"># Train the model using the training sets and check score<\/code><\/div>\n<div class=\"line number3 index2 alt2\"><code class=\"text plain\">logistic &lt;- glm(y_train ~ ., data = x,family='binomial')<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"text plain\">summary(logistic)<\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"text plain\">#Predict Output<\/code><\/div>\n<div class=\"line number7 index6 alt2\"><code class=\"text plain\">predicted= predict(logistic,x_test)<\/code><\/div>\n<\/blockquote>\n<p><strong>\u66f4\u8fdb\u4e00\u6b65\uff1a<\/strong><\/p>\n<p>\u4f60\u53ef\u4ee5\u5c1d\u8bd5\u66f4\u591a\u7684\u65b9\u6cd5\u6765\u6539\u8fdb\u8fd9\u4e2a\u6a21\u578b\uff1a<\/p>\n<blockquote>\n<ul class=\" list-paddingleft-2\">\n<li>\u52a0\u5165\u4ea4\u4e92\u9879<\/li>\n<li>\u7cbe\u7b80\u6a21\u578b\u7279\u6027<\/li>\n<li>\u4f7f\u7528\u6b63\u5219\u5316\u65b9\u6cd5<\/li>\n<li>\u4f7f\u7528\u975e\u7ebf\u6027\u6a21\u578b<\/li>\n<\/ul>\n<\/blockquote>\n<p><strong>3\u3001\u51b3\u7b56\u6811<\/strong><\/p>\n<p>\u8fd9\u662f\u6211\u6700\u559c\u7231\u4e5f\u662f\u6700\u9891\u7e41\u4f7f\u7528\u7684\u7b97\u6cd5\u4e4b\u4e00\u3002\u8fd9\u4e2a\u76d1\u7763\u5f0f\u5b66\u4e60\u7b97\u6cd5\u901a\u5e38\u88ab\u7528\u4e8e\u5206\u7c7b\u95ee\u9898\u3002\u4ee4\u4eba\u60ca\u5947\u7684\u662f\uff0c\u5b83\u540c\u65f6\u9002\u7528\u4e8e\u5206\u7c7b\u53d8\u91cf\u548c\u8fde\u7eed\u56e0\u53d8\u91cf\u3002\u5728\u8fd9\u4e2a\u7b97\u6cd5\u4e2d\uff0c\u6211\u4eec\u5c06\u603b\u4f53\u5206\u6210\u4e24\u4e2a\u6216\u66f4\u591a\u7684\u540c\u7c7b\u7fa4\u3002\u8fd9\u662f\u6839\u636e\u6700\u91cd\u8981\u7684\u5c5e\u6027\u6216\u8005\u81ea\u53d8\u91cf\u6765\u5206\u6210\u5c3d\u53ef\u80fd\u4e0d\u540c\u7684\u7ec4\u522b\u3002\u60f3\u8981\u77e5\u9053\u66f4\u591a\uff0c\u53ef\u4ee5\u9605\u8bfb\uff1a\u7b80\u5316\u51b3\u7b56\u6811\u3002<\/p>\n<p><img decoding=\"async\" src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/fhujzoQe7TrlSXGhVbPSbH11K9Q6JTGex08zHiarP11rE4aeF3rudhJ7PzlPKNmN4FibcaVmID15k0JdTLgLkBgQ\/640?wx_fmt=jpeg&amp;wxfrom=5&amp;wx_lazy=1\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/fhujzoQe7TrlSXGhVbPSbH11K9Q6JTGex08zHiarP11rE4aeF3rudhJ7PzlPKNmN4FibcaVmID15k0JdTLgLkBgQ\/0?wx_fmt=jpeg\" data-type=\"jpeg\" data-ratio=\"0.7351778656126482\" data-w=\"\" \/><\/p>\n<p>\u6765\u6e90\uff1a statsexchange<\/p>\n<p>\u5728\u4e0a\u56fe\u4e2d\u4f60\u53ef\u4ee5\u770b\u5230\uff0c\u6839\u636e\u591a\u79cd\u5c5e\u6027\uff0c\u4eba\u7fa4\u88ab\u5206\u6210\u4e86\u4e0d\u540c\u7684\u56db\u4e2a\u5c0f\u7ec4\uff0c\u6765\u5224\u65ad \u201c\u4ed6\u4eec\u4f1a\u4e0d\u4f1a\u53bb\u73a9\u201d\u3002\u4e3a\u4e86\u628a\u603b\u4f53\u5206\u6210\u4e0d\u540c\u7ec4\u522b\uff0c\u9700\u8981\u7528\u5230\u8bb8\u591a\u6280\u672f\uff0c\u6bd4\u5982\u8bf4 Gini\u3001Information Gain\u3001Chi-square\u3001entropy\u3002<\/p>\n<p>\u7406\u89e3\u51b3\u7b56\u6811\u5de5\u4f5c\u673a\u5236\u7684\u6700\u597d\u65b9\u5f0f\u662f\u73a9Jezzball\uff0c\u4e00\u4e2a\u5fae\u8f6f\u7684\u7ecf\u5178\u6e38\u620f\uff08\u89c1\u4e0b\u56fe\uff09\u3002\u8fd9\u4e2a\u6e38\u620f\u7684\u6700\u7ec8\u76ee\u7684\uff0c\u662f\u5728\u4e00\u4e2a\u53ef\u4ee5\u79fb\u52a8\u5899\u58c1\u7684\u623f\u95f4\u91cc\uff0c\u901a\u8fc7\u9020\u5899\u6765\u5206\u5272\u51fa\u6ca1\u6709\u5c0f\u7403\u7684\u3001\u5c3d\u91cf\u5927\u7684\u7a7a\u95f4\u3002<\/p>\n<p><img decoding=\"async\" src=\"data:image\/png;base64,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\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/fhujzoQe7TrlSXGhVbPSbH11K9Q6JTGePgLIPy4ymeibqWnnIeVrZaSJ9XlyoXgyklGzV5ytHzeB6fXUGOmpWHg\/0?wx_fmt=jpeg\" data-type=\"jpeg\" data-ratio=\"0.6\" data-w=\"290\" \/><\/p>\n<p>\u56e0\u6b64\uff0c\u6bcf\u4e00\u6b21\u4f60\u7528\u5899\u58c1\u6765\u5206\u9694\u623f\u95f4\u65f6\uff0c\u90fd\u662f\u5728\u5c1d\u8bd5\u7740\u5728\u540c\u4e00\u95f4\u623f\u91cc\u521b\u5efa\u4e24\u4e2a\u4e0d\u540c\u7684\u603b\u4f53\u3002\u76f8\u4f3c\u5730\uff0c\u51b3\u7b56\u6811\u4e5f\u5728\u628a\u603b\u4f53\u5c3d\u91cf\u5206\u5272\u5230\u4e0d\u540c\u7684\u7ec4\u91cc\u53bb\u3002<\/p>\n<p>\u66f4\u591a\u4fe1\u606f\u8bf7\u89c1\uff1a\u51b3\u7b56\u6811\u7b97\u6cd5\u7684\u7b80\u5316<\/p>\n<p>Python\u4ee3\u7801<\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"python comments\">#Import Library<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"python comments\">#Import other necessary libraries like pandas, numpy...<\/code><\/div>\n<div class=\"line number3 index2 alt2\"><code class=\"python keyword\">from<\/code> <code class=\"python plain\">sklearn <\/code><code class=\"python keyword\">import<\/code> <code class=\"python plain\">tree<\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"python comments\">#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset<\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"python comments\"># Create tree object <\/code><\/div>\n<div class=\"line number7 index6 alt2\"><code class=\"python plain\">model <\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">tree.DecisionTreeClassifier(criterion<\/code><code class=\"python keyword\">=<\/code><code class=\"python string\">'gini'<\/code><code class=\"python plain\">) <\/code><code class=\"python comments\"># for classification, here you can change the algorithm as gini or entropy (information gain) by default it is gini <\/code><\/div>\n<div class=\"line number9 index8 alt2\"><code class=\"python comments\"># model = tree.DecisionTreeRegressor() for regression<\/code><\/div>\n<div class=\"line number10 index9 alt1\"><code class=\"python comments\"># Train the model using the training sets and check score<\/code><\/div>\n<div class=\"line number11 index10 alt2\"><code class=\"python plain\">model.fit(X, y)<\/code><\/div>\n<div class=\"line number12 index11 alt1\"><code class=\"python plain\">model.score(X, y)<\/code><\/div>\n<div class=\"line number14 index13 alt1\"><code class=\"python comments\">#Predict Output<\/code><\/div>\n<div class=\"line number15 index14 alt2\"><code class=\"python plain\">predicted<\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">model.predict(x_test)<\/code><\/div>\n<\/blockquote>\n<p><strong>R\u8bed\u8a00<\/strong><\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"text plain\">library(rpart)<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"text plain\">x &lt;- cbind(x_train,y_train)<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"text plain\"># grow tree <\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"text plain\">fit &lt;- rpart(y_train ~ ., data = x,method=\"class\")<\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"text plain\">summary(fit)<\/code><\/div>\n<div class=\"line number8 index7 alt1\"><code class=\"text plain\">#Predict Output <\/code><\/div>\n<div class=\"line number9 index8 alt2\"><code class=\"text plain\">predicted= predict(fit,x_test)<\/code><\/div>\n<\/blockquote>\n<p><strong>4\u3001\u652f\u6301\u5411\u91cf\u673a<\/strong><\/p>\n<p>\u8fd9\u662f\u4e00\u79cd\u5206\u7c7b\u65b9\u6cd5\u3002\u5728\u8fd9\u4e2a\u7b97\u6cd5\u4e2d\uff0c\u6211\u4eec\u5c06\u6bcf\u4e2a\u6570\u636e\u5728N\u7ef4\u7a7a\u95f4\u4e2d\u7528\u70b9\u6807\u51fa\uff08N\u662f\u4f60\u6240\u6709\u7684\u7279\u5f81\u603b\u6570\uff09\uff0c\u6bcf\u4e2a\u7279\u5f81\u7684\u503c\u662f\u4e00\u4e2a\u5750\u6807\u7684\u503c\u3002<\/p>\n<p>\u4e3e\u4e2a\u4f8b\u5b50\uff0c\u5982\u679c\u6211\u4eec\u53ea\u6709\u8eab\u9ad8\u548c\u5934\u53d1\u957f\u5ea6\u4e24\u4e2a\u7279\u5f81\uff0c\u6211\u4eec\u4f1a\u5728\u4e8c\u7ef4\u7a7a\u95f4\u4e2d\u6807\u51fa\u8fd9\u4e24\u4e2a\u53d8\u91cf\uff0c\u6bcf\u4e2a\u70b9\u6709\u4e24\u4e2a\u5750\u6807\uff08\u8fd9\u4e9b\u5750\u6807\u53eb\u505a\u652f\u6301\u5411\u91cf\uff09\u3002<\/p>\n<p><img decoding=\"async\" src=\"data:image\/png;base64,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\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/fhujzoQe7TrlSXGhVbPSbH11K9Q6JTGeibj6qIT2j9JtGT2OnFmyKRXpp70x2WUkLYYRJNZgjlxyvgfCR0JQURw\/0?wx_fmt=jpeg\" data-type=\"jpeg\" data-ratio=\"0.6758893280632411\" data-w=\"\" \/><\/p>\n<p>\u73b0\u5728\uff0c\u6211\u4eec\u4f1a\u627e\u5230\u5c06\u4e24\u7ec4\u4e0d\u540c\u6570\u636e\u5206\u5f00\u7684\u4e00\u6761\u76f4\u7ebf\u3002\u4e24\u4e2a\u5206\u7ec4\u4e2d\u8ddd\u79bb\u6700\u8fd1\u7684\u4e24\u4e2a\u70b9\u5230\u8fd9\u6761\u7ebf\u7684\u8ddd\u79bb\u540c\u65f6\u6700\u4f18\u5316\u3002<\/p>\n<p><img decoding=\"async\" src=\"data:image\/png;base64,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\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/fhujzoQe7TrlSXGhVbPSbH11K9Q6JTGejTFUjbmLsd02L9NeSwlY52icAXnGLYaU4kAwJoqO0nv71vteqPJfhBA\/0?wx_fmt=jpeg\" data-type=\"jpeg\" data-ratio=\"0.68\" data-w=\"300\" \/><\/p>\n<p>\u4e0a\u9762\u793a\u4f8b\u4e2d\u7684\u9ed1\u7ebf\u5c06\u6570\u636e\u5206\u7c7b\u4f18\u5316\u6210\u4e24\u4e2a\u5c0f\u7ec4\uff0c\u4e24\u7ec4\u4e2d\u8ddd\u79bb\u6700\u8fd1\u7684\u70b9\uff08\u56fe\u4e2dA\u3001B\u70b9\uff09\u5230\u8fbe\u9ed1\u7ebf\u7684\u8ddd\u79bb\u6ee1\u8db3\u6700\u4f18\u6761\u4ef6\u3002\u8fd9\u6761\u76f4\u7ebf\u5c31\u662f\u6211\u4eec\u7684\u5206\u5272\u7ebf\u3002\u63a5\u4e0b\u6765\uff0c\u6d4b\u8bd5\u6570\u636e\u843d\u5230\u76f4\u7ebf\u7684\u54ea\u4e00\u8fb9\uff0c\u6211\u4eec\u5c31\u5c06\u5b83\u5206\u5230\u54ea\u4e00\u7c7b\u53bb\u3002<\/p>\n<p>\u66f4\u591a\u8bf7\u89c1\uff1a\u652f\u6301\u5411\u91cf\u673a\u7684\u7b80\u5316<\/p>\n<p>\u5c06\u8fd9\u4e2a\u7b97\u6cd5\u60f3\u4f5c\u662f\u5728\u4e00\u4e2a N \u7ef4\u7a7a\u95f4\u73a9 JezzBall\u3002\u9700\u8981\u5bf9\u6e38\u620f\u505a\u4e00\u4e9b\u5c0f\u53d8\u52a8\uff1a<\/p>\n<blockquote>\n<ul class=\" list-paddingleft-2\">\n<li>\u6bd4\u8d77\u4e4b\u524d\u53ea\u80fd\u5728\u6c34\u5e73\u65b9\u5411\u6216\u8005\u7ad6\u76f4\u65b9\u5411\u753b\u76f4\u7ebf\uff0c\u73b0\u5728\u4f60\u53ef\u4ee5\u5728\u4efb\u610f\u89d2\u5ea6\u753b\u7ebf\u6216\u5e73\u9762\u3002<\/li>\n<li>\u6e38\u620f\u7684\u76ee\u7684\u53d8\u6210\u628a\u4e0d\u540c\u989c\u8272\u7684\u7403\u5206\u5272\u5728\u4e0d\u540c\u7684\u7a7a\u95f4\u91cc\u3002<\/li>\n<li>\u7403\u7684\u4f4d\u7f6e\u4e0d\u4f1a\u6539\u53d8\u3002<\/li>\n<\/ul>\n<\/blockquote>\n<p><strong>Python\u4ee3\u7801<\/strong><\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"python comments\">#Import Library<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"python keyword\">from<\/code> <code class=\"python plain\">sklearn <\/code><code class=\"python keyword\">import<\/code> <code class=\"python plain\">svm<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"python comments\">#Assumed you have, X (predic<\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"python plain\">tor) <\/code><code class=\"python keyword\">and<\/code> <code class=\"python plain\">Y (target) <\/code><code class=\"python keyword\">for<\/code> <code class=\"python plain\">training data <\/code><code class=\"python functions\">set<\/code> <code class=\"python keyword\">and<\/code> <code class=\"python plain\">x_test(predictor) of test_dataset<\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"python comments\"># Create SVM classification object <\/code><\/div>\n<div class=\"line number7 index6 alt2\"><code class=\"python plain\">model <\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">svm.svc() <\/code><code class=\"python comments\"># there is various option associated with it, this is simple for classification. You can refer link, for mo# re detail.<\/code><\/div>\n<div class=\"line number8 index7 alt1\"><code class=\"python comments\"># Train the model using the training sets and check score<\/code><\/div>\n<div class=\"line number9 index8 alt2\"><code class=\"python plain\">model.fit(X, y)<\/code><\/div>\n<div class=\"line number10 index9 alt1\"><code class=\"python plain\">model.score(X, y)<\/code><\/div>\n<div class=\"line number12 index11 alt1\"><code class=\"python comments\">#Predict Output<\/code><\/div>\n<div class=\"line number13 index12 alt2\"><code class=\"python plain\">predicted<\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">model.predict(x_test)<\/code><\/div>\n<\/blockquote>\n<p><strong>R\u4ee3\u7801<\/strong><\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"text plain\">library(e1071)<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"text plain\">x &lt;- cbind(x_train,y_train)<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"text plain\"># Fitting model<\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"text plain\">fit &lt;-svm(y_train ~ ., data = x)<\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"text plain\">summary(fit)<\/code><\/div>\n<div class=\"line number8 index7 alt1\"><code class=\"text plain\">#Predict Output <\/code><\/div>\n<div class=\"line number9 index8 alt2\"><code class=\"text plain\">predicted= predict(fit,x_test)<\/code><\/div>\n<\/blockquote>\n<p><strong>5\u3001\u6734\u7d20\u8d1d\u53f6\u65af<\/strong><\/p>\n<p>\u5728\u9884\u793a\u53d8\u91cf\u95f4\u76f8\u4e92\u72ec\u7acb\u7684\u524d\u63d0\u4e0b\uff0c\u6839\u636e\u8d1d\u53f6\u65af\u5b9a\u7406\u53ef\u4ee5\u5f97\u5230\u6734\u7d20\u8d1d\u53f6\u65af\u8fd9\u4e2a\u5206\u7c7b\u65b9\u6cd5\u3002\u7528\u66f4\u7b80\u5355\u7684\u8bdd\u6765\u8bf4\uff0c\u4e00\u4e2a\u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\u5668\u5047\u8bbe\u4e00\u4e2a\u5206\u7c7b\u7684\u7279\u6027\u4e0e\u8be5\u5206\u7c7b\u7684\u5176\u5b83\u7279\u6027\u4e0d\u76f8\u5173\u3002\u4e3e\u4e2a\u4f8b\u5b50\uff0c\u5982\u679c\u4e00\u4e2a\u6c34\u679c\u53c8\u5706\u53c8\u7ea2\uff0c\u5e76\u4e14\u76f4\u5f84\u5927\u7ea6\u662f 3 \u82f1\u5bf8\uff0c\u90a3\u4e48\u8fd9\u4e2a\u6c34\u679c\u53ef\u80fd\u4f1a\u662f\u82f9\u679c\u3002\u5373\u4fbf\u8fd9\u4e9b\u7279\u6027\u4e92\u76f8\u4f9d\u8d56\uff0c\u6216\u8005\u4f9d\u8d56\u4e8e\u522b\u7684\u7279\u6027\u7684\u5b58\u5728\uff0c\u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\u5668\u8fd8\u662f\u4f1a\u5047\u8bbe\u8fd9\u4e9b\u7279\u6027\u5206\u522b\u72ec\u7acb\u5730\u6697\u793a\u8fd9\u4e2a\u6c34\u679c\u662f\u4e2a\u82f9\u679c\u3002<\/p>\n<p>\u6734\u7d20\u8d1d\u53f6\u65af\u6a21\u578b\u6613\u4e8e\u5efa\u9020\uff0c\u4e14\u5bf9\u4e8e\u5927\u578b\u6570\u636e\u96c6\u975e\u5e38\u6709\u7528\u3002\u867d\u7136\u7b80\u5355\uff0c\u4f46\u662f\u6734\u7d20\u8d1d\u53f6\u65af\u7684\u8868\u73b0\u5374\u8d85\u8d8a\u4e86\u975e\u5e38\u590d\u6742\u7684\u5206\u7c7b\u65b9\u6cd5\u3002<\/p>\n<p>\u8d1d\u53f6\u65af\u5b9a\u7406\u63d0\u4f9b\u4e86\u4e00\u79cd\u4eceP(c)\u3001P(x)\u548cP(x|c) \u8ba1\u7b97\u540e\u9a8c\u6982\u7387 P(c|x) \u7684\u65b9\u6cd5\u3002\u8bf7\u770b\u4ee5\u4e0b\u7b49\u5f0f\uff1a<\/p>\n<p><img decoding=\"async\" src=\"data:image\/png;base64,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\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/fhujzoQe7TrlSXGhVbPSbH11K9Q6JTGeI4ibEt35qE5pO67UBWfnVzK7cbYsZzDUkCuRI87ze8jDQIGTWzLnFCA\/0?wx_fmt=jpeg\" data-type=\"jpeg\" data-ratio=\"0.5733333333333334\" data-w=\"300\" \/><\/p>\n<p>\u5728\u8fd9\u91cc\uff0c<\/p>\n<blockquote>\n<ul class=\" list-paddingleft-2\">\n<li>P(c|x) \u662f\u5df2\u77e5\u9884\u793a\u53d8\u91cf\uff08\u5c5e\u6027\uff09\u7684\u524d\u63d0\u4e0b\uff0c\u7c7b\uff08\u76ee\u6807\uff09\u7684\u540e\u9a8c\u6982\u7387<\/li>\n<li>P(c) \u662f\u7c7b\u7684\u5148\u9a8c\u6982\u7387<\/li>\n<li>P(x|c) \u662f\u53ef\u80fd\u6027\uff0c\u5373\u5df2\u77e5\u7c7b\u7684\u524d\u63d0\u4e0b\uff0c\u9884\u793a\u53d8\u91cf\u7684\u6982\u7387<\/li>\n<li>P(x) \u662f\u9884\u793a\u53d8\u91cf\u7684\u5148\u9a8c\u6982\u7387<\/li>\n<\/ul>\n<\/blockquote>\n<p>\u4f8b\u5b50\uff1a\u8ba9\u6211\u4eec\u7528\u4e00\u4e2a\u4f8b\u5b50\u6765\u7406\u89e3\u8fd9\u4e2a\u6982\u5ff5\u3002\u5728\u4e0b\u9762\uff0c\u6211\u6709\u4e00\u4e2a\u5929\u6c14\u7684\u8bad\u7ec3\u96c6\u548c\u5bf9\u5e94\u7684\u76ee\u6807\u53d8\u91cf\u201cPlay\u201d\u3002\u73b0\u5728\uff0c\u6211\u4eec\u9700\u8981\u6839\u636e\u5929\u6c14\u60c5\u51b5\uff0c\u5c06\u4f1a\u201c\u73a9\u201d\u548c\u201c\u4e0d\u73a9\u201d\u7684\u53c2\u4e0e\u8005\u8fdb\u884c\u5206\u7c7b\u3002\u8ba9\u6211\u4eec\u6267\u884c\u4ee5\u4e0b\u6b65\u9aa4\u3002<\/p>\n<p>\u6b65\u9aa41\uff1a\u628a\u6570\u636e\u96c6\u8f6c\u6362\u6210\u9891\u7387\u8868\u3002<\/p>\n<p>\u6b65\u9aa42\uff1a\u5229\u7528\u7c7b\u4f3c\u201c\u5f53Overcast\u53ef\u80fd\u6027\u4e3a0.29\u65f6\uff0c\u73a9\u800d\u7684\u53ef\u80fd\u6027\u4e3a0.64\u201d\u8fd9\u6837\u7684\u6982\u7387\uff0c\u521b\u9020 Likelihood \u8868\u683c\u3002<\/p>\n<p><img decoding=\"async\" src=\"data:image\/png;base64,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\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/fhujzoQe7TrlSXGhVbPSbH11K9Q6JTGe3LLq6bpbFM6sREwcrib4kUmYVcoJqJ2V4yGyicnl08e0TfgZwmZA4XDw\/0?wx_fmt=jpeg\" data-type=\"jpeg\" data-ratio=\"0.36561264822134387\" data-w=\"\" \/><\/p>\n<p>\u6b65\u9aa43\uff1a\u73b0\u5728\uff0c\u4f7f\u7528\u6734\u7d20\u8d1d\u53f6\u65af\u7b49\u5f0f\u6765\u8ba1\u7b97\u6bcf\u4e00\u7c7b\u7684\u540e\u9a8c\u6982\u7387\u3002\u540e\u9a8c\u6982\u7387\u6700\u5927\u7684\u7c7b\u5c31\u662f\u9884\u6d4b\u7684\u7ed3\u679c\u3002<\/p>\n<p>\u95ee\u9898\uff1a\u5982\u679c\u5929\u6c14\u6674\u6717\uff0c\u53c2\u4e0e\u8005\u5c31\u80fd\u73a9\u800d\u3002\u8fd9\u4e2a\u9648\u8ff0\u6b63\u786e\u5417\uff1f<\/p>\n<p>\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8ba8\u8bba\u8fc7\u7684\u65b9\u6cd5\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u3002\u4e8e\u662f P\uff08\u4f1a\u73a9 | \u6674\u6717\uff09= P\uff08\u6674\u6717 | \u4f1a\u73a9\uff09* P\uff08\u4f1a\u73a9\uff09\/ P \uff08\u6674\u6717\uff09<\/p>\n<p>\u6211\u4eec\u6709 P \uff08\u6674\u6717 |\u4f1a\u73a9\uff09= 3\/9 = 0.33\uff0cP\uff08\u6674\u6717\uff09 = 5\/14 = 0.36, P\uff08\u4f1a\u73a9\uff09= 9\/14 = 0.64<\/p>\n<p>\u73b0\u5728\uff0cP(\u4f1a\u73a9 | \u6674\u6717\uff09= 0.33 * 0.64 \/ 0.36 = 0.60\uff0c\u6709\u66f4\u5927\u7684\u6982\u7387\u3002<\/p>\n<p>\u6734\u7d20\u8d1d\u53f6\u65af\u4f7f\u7528\u4e86\u4e00\u4e2a\u76f8\u4f3c\u7684\u65b9\u6cd5\uff0c\u901a\u8fc7\u4e0d\u540c\u5c5e\u6027\u6765\u9884\u6d4b\u4e0d\u540c\u7c7b\u522b\u7684\u6982\u7387\u3002\u8fd9\u4e2a\u7b97\u6cd5\u901a\u5e38\u88ab\u7528\u4e8e\u6587\u672c\u5206\u7c7b\uff0c\u4ee5\u53ca\u6d89\u53ca\u5230\u591a\u4e2a\u7c7b\u7684\u95ee\u9898\u3002<\/p>\n<p>Python\u4ee3\u7801<\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"python comments\">#Import Library<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"python keyword\">from<\/code> <code class=\"python plain\">sklearn.naive_bayes <\/code><code class=\"python keyword\">import<\/code> <code class=\"python plain\">GaussianNB<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"python comments\">#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset<\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"python comments\"># Create SVM classification object model = GaussianNB() # there is other distribution for multinomial classes like Bernoulli Naive Bayes, Refer link<\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"python comments\"># Train the model using the training sets and check score<\/code><\/div>\n<div class=\"line number7 index6 alt2\"><code class=\"python plain\">model.fit(X, y)<\/code><\/div>\n<div class=\"line number9 index8 alt2\"><code class=\"python comments\">#Predict Output<\/code><\/div>\n<div class=\"line number10 index9 alt1\"><code class=\"python plain\">predicted<\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">model.predict(x_test)<\/code><\/div>\n<\/blockquote>\n<p><strong>R\u4ee3\u7801<\/strong><\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"text plain\">library(e1071)<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"text plain\">x &lt;- cbind(x_train,y_train)<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"text plain\"># Fitting model<\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"text plain\">fit &lt;-naiveBayes(y_train ~ ., data = x)<\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"text plain\">summary(fit)<\/code><\/div>\n<div class=\"line number8 index7 alt1\"><code class=\"text plain\">#Predict Output <\/code><\/div>\n<div class=\"line number9 index8 alt2\"><code class=\"text plain\">predicted= predict(fit,x_test)<\/code><\/div>\n<\/blockquote>\n<p><strong>6\u3001KNN\uff08K \u2013 \u6700\u8fd1\u90bb\u7b97\u6cd5\uff09<\/strong><\/p>\n<p>\u8be5\u7b97\u6cd5\u53ef\u7528\u4e8e\u5206\u7c7b\u95ee\u9898\u548c\u56de\u5f52\u95ee\u9898\u3002\u7136\u800c\uff0c\u5728\u4e1a\u754c\u5185\uff0cK \u2013 \u6700\u8fd1\u90bb\u7b97\u6cd5\u66f4\u5e38\u7528\u4e8e\u5206\u7c7b\u95ee\u9898\u3002K \u2013 \u6700\u8fd1\u90bb\u7b97\u6cd5\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u7b97\u6cd5\u3002\u5b83\u50a8\u5b58\u6240\u6709\u7684\u6848\u4f8b\uff0c\u901a\u8fc7\u5468\u56f4k\u4e2a\u6848\u4f8b\u4e2d\u7684\u5927\u591a\u6570\u60c5\u51b5\u5212\u5206\u65b0\u7684\u6848\u4f8b\u3002\u6839\u636e\u4e00\u4e2a\u8ddd\u79bb\u51fd\u6570\uff0c\u65b0\u6848\u4f8b\u4f1a\u88ab\u5206\u914d\u5230\u5b83\u7684 K \u4e2a\u8fd1\u90bb\u4e2d\u6700\u666e\u904d\u7684\u7c7b\u522b\u4e2d\u53bb\u3002<\/p>\n<p>\u8fd9\u4e9b\u8ddd\u79bb\u51fd\u6570\u53ef\u4ee5\u662f\u6b27\u5f0f\u8ddd\u79bb\u3001\u66fc\u54c8\u987f\u8ddd\u79bb\u3001\u660e\u5f0f\u8ddd\u79bb\u6216\u8005\u662f\u6c49\u660e\u8ddd\u79bb\u3002\u524d\u4e09\u4e2a\u8ddd\u79bb\u51fd\u6570\u7528\u4e8e\u8fde\u7eed\u51fd\u6570\uff0c\u7b2c\u56db\u4e2a\u51fd\u6570\uff08\u6c49\u660e\u51fd\u6570\uff09\u5219\u88ab\u7528\u4e8e\u5206\u7c7b\u53d8\u91cf\u3002\u5982\u679c K=1\uff0c\u65b0\u6848\u4f8b\u5c31\u76f4\u63a5\u88ab\u5206\u5230\u79bb\u5176\u6700\u8fd1\u7684\u6848\u4f8b\u6240\u5c5e\u7684\u7c7b\u522b\u4e2d\u3002\u6709\u65f6\u5019\uff0c\u4f7f\u7528 KNN \u5efa\u6a21\u65f6\uff0c\u9009\u62e9 K \u7684\u53d6\u503c\u662f\u4e00\u4e2a\u6311\u6218\u3002<\/p>\n<p>\u66f4\u591a\u4fe1\u606f\uff1aK \u2013 \u6700\u8fd1\u90bb\u7b97\u6cd5\u5165\u95e8\uff08\u7b80\u5316\u7248\uff09<\/p>\n<p><img decoding=\"async\" src=\"data:image\/png;base64,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\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/fhujzoQe7TrlSXGhVbPSbH11K9Q6JTGepT6jDQ71d4r29Mc3WnULl7LBcpRbOG3WU8LqFd7MnjZkXCibZjiaQgOg\/0?wx_fmt=jpeg\" data-type=\"jpeg\" data-ratio=\"0.4367588932806324\" data-w=\"\" \/><\/p>\n<p>\u6211\u4eec\u53ef\u4ee5\u5f88\u5bb9\u6613\u5730\u5728\u73b0\u5b9e\u751f\u6d3b\u4e2d\u5e94\u7528\u5230 KNN\u3002\u5982\u679c\u60f3\u8981\u4e86\u89e3\u4e00\u4e2a\u5b8c\u5168\u964c\u751f\u7684\u4eba\uff0c\u4f60\u4e5f\u8bb8\u60f3\u8981\u53bb\u627e\u4ed6\u7684\u597d\u670b\u53cb\u4eec\u6216\u8005\u4ed6\u7684\u5708\u5b50\u6765\u83b7\u5f97\u4ed6\u7684\u4fe1\u606f\u3002<\/p>\n<p>\u5728\u9009\u62e9\u4f7f\u7528 KNN \u4e4b\u524d\uff0c\u4f60\u9700\u8981\u8003\u8651\u7684\u4e8b\u60c5\uff1a<\/p>\n<blockquote>\n<ul class=\" list-paddingleft-2\">\n<li>KNN \u7684\u8ba1\u7b97\u6210\u672c\u5f88\u9ad8\u3002<\/li>\n<li>\u53d8\u91cf\u5e94\u8be5\u5148\u6807\u51c6\u5316\uff08normalized\uff09\uff0c\u4e0d\u7136\u4f1a\u88ab\u66f4\u9ad8\u8303\u56f4\u7684\u53d8\u91cf\u504f\u501a\u3002<\/li>\n<li>\u5728\u4f7f\u7528KNN\u4e4b\u524d\uff0c\u8981\u5728\u91ce\u503c\u53bb\u9664\u548c\u566a\u97f3\u53bb\u9664\u7b49\u524d\u671f\u5904\u7406\u591a\u82b1\u529f\u592b\u3002<\/li>\n<\/ul>\n<\/blockquote>\n<p><strong>Python\u4ee3\u7801<\/strong><\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"python comments\">#Import Library<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"python keyword\">from<\/code> <code class=\"python plain\">sklearn.neighbors <\/code><code class=\"python keyword\">import<\/code> <code class=\"python plain\">KNeighborsClassifier<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"python comments\">#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset<\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"python comments\"># Create KNeighbors classifier object model <\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"python plain\">KNeighborsClassifier(n_neighbors<\/code><code class=\"python keyword\">=<\/code><code class=\"python value\">6<\/code><code class=\"python plain\">) <\/code><code class=\"python comments\"># default value for n_neighbors is 5<\/code><\/div>\n<div class=\"line number8 index7 alt1\"><code class=\"python comments\"># Train the model using the training sets and check score<\/code><\/div>\n<div class=\"line number9 index8 alt2\"><code class=\"python plain\">model.fit(X, y)<\/code><\/div>\n<div class=\"line number11 index10 alt2\"><code class=\"python comments\">#Predict Output<\/code><\/div>\n<div class=\"line number12 index11 alt1\"><code class=\"python plain\">predicted<\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">model.predict(x_test)<\/code><\/div>\n<\/blockquote>\n<p><strong>R\u4ee3\u7801<\/strong><\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"text plain\">library(knn)<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"text plain\">x &lt;- cbind(x_train,y_train)<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"text plain\"># Fitting model<\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"text plain\">fit &lt;-knn(y_train ~ ., data = x,k=5)<\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"text plain\">summary(fit)<\/code><\/div>\n<div class=\"line number8 index7 alt1\"><code class=\"text plain\">#Predict Output <\/code><\/div>\n<div class=\"line number9 index8 alt2\"><code class=\"text plain\">predicted= predict(fit,x_test)<\/code><\/div>\n<\/blockquote>\n<p><strong>7\u3001K \u5747\u503c\u7b97\u6cd5<br \/>\n<\/strong><\/p>\n<p>K \u2013 \u5747\u503c\u7b97\u6cd5\u662f\u4e00\u79cd\u975e\u76d1\u7763\u5f0f\u5b66\u4e60\u7b97\u6cd5\uff0c\u5b83\u80fd\u89e3\u51b3\u805a\u7c7b\u95ee\u9898\u3002\u4f7f\u7528 K \u2013 \u5747\u503c\u7b97\u6cd5\u6765\u5c06\u4e00\u4e2a\u6570\u636e\u5f52\u5165\u4e00\u5b9a\u6570\u91cf\u7684\u96c6\u7fa4\uff08\u5047\u8bbe\u6709 k \u4e2a\u96c6\u7fa4\uff09\u7684\u8fc7\u7a0b\u662f\u7b80\u5355\u7684\u3002\u4e00\u4e2a\u96c6\u7fa4\u5185\u7684\u6570\u636e\u70b9\u662f\u5747\u5300\u9f50\u6b21\u7684\uff0c\u5e76\u4e14\u5f02\u4e8e\u522b\u7684\u96c6\u7fa4\u3002<\/p>\n<p>\u8fd8\u8bb0\u5f97\u4ece\u58a8\u6c34\u6e0d\u91cc\u627e\u51fa\u5f62\u72b6\u7684\u6d3b\u52a8\u5417\uff1fK \u2013 \u5747\u503c\u7b97\u6cd5\u5728\u67d0\u65b9\u9762\u7c7b\u4f3c\u4e8e\u8fd9\u4e2a\u6d3b\u52a8\u3002\u89c2\u5bdf\u5f62\u72b6\uff0c\u5e76\u5ef6\u4f38\u60f3\u8c61\u6765\u627e\u51fa\u5230\u5e95\u6709\u591a\u5c11\u79cd\u96c6\u7fa4\u6216\u8005\u603b\u4f53\u3002<\/p>\n<p><img decoding=\"async\" src=\"data:image\/png;base64,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\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/fhujzoQe7TrlSXGhVbPSbH11K9Q6JTGe4cVA1AEABOGibOSzUBQJprZ5bODfTicStqpNNJsEKDNFrAIdYIl9vn3A\/0?wx_fmt=jpeg\" data-type=\"jpeg\" data-ratio=\"1.056338028169014\" data-w=\"284\" \/><\/p>\n<p>K \u2013 \u5747\u503c\u7b97\u6cd5\u600e\u6837\u5f62\u6210\u96c6\u7fa4\uff1a<\/p>\n<blockquote>\n<ul class=\" list-paddingleft-2\">\n<li>K \u2013 \u5747\u503c\u7b97\u6cd5\u7ed9\u6bcf\u4e2a\u96c6\u7fa4\u9009\u62e9k\u4e2a\u70b9\u3002\u8fd9\u4e9b\u70b9\u79f0\u4f5c\u4e3a\u8d28\u5fc3\u3002<\/li>\n<li>\u6bcf\u4e00\u4e2a\u6570\u636e\u70b9\u4e0e\u8ddd\u79bb\u6700\u8fd1\u7684\u8d28\u5fc3\u5f62\u6210\u4e00\u4e2a\u96c6\u7fa4\uff0c\u4e5f\u5c31\u662f k \u4e2a\u96c6\u7fa4\u3002<\/li>\n<li>\u6839\u636e\u73b0\u6709\u7684\u7c7b\u522b\u6210\u5458\uff0c\u627e\u51fa\u6bcf\u4e2a\u7c7b\u522b\u7684\u8d28\u5fc3\u3002\u73b0\u5728\u6211\u4eec\u6709\u4e86\u65b0\u8d28\u5fc3\u3002<\/li>\n<li>\u5f53\u6211\u4eec\u6709\u65b0\u8d28\u5fc3\u540e\uff0c\u91cd\u590d\u6b65\u9aa4 2 \u548c\u6b65\u9aa4 3\u3002\u627e\u5230\u8ddd\u79bb\u6bcf\u4e2a\u6570\u636e\u70b9\u6700\u8fd1\u7684\u8d28\u5fc3\uff0c\u5e76\u4e0e\u65b0\u7684k\u96c6\u7fa4\u8054\u7cfb\u8d77\u6765\u3002\u91cd\u590d\u8fd9\u4e2a\u8fc7\u7a0b\uff0c\u76f4\u5230\u6570\u636e\u90fd\u6536\u655b\u4e86\uff0c\u4e5f\u5c31\u662f\u5f53\u8d28\u5fc3\u4e0d\u518d\u6539\u53d8\u3002<\/li>\n<\/ul>\n<\/blockquote>\n<p><strong>\u5982\u4f55\u51b3\u5b9a K \u503c\uff1a<\/strong><\/p>\n<p>K \u2013 \u5747\u503c\u7b97\u6cd5\u6d89\u53ca\u5230\u96c6\u7fa4\uff0c\u6bcf\u4e2a\u96c6\u7fa4\u6709\u81ea\u5df1\u7684\u8d28\u5fc3\u3002\u4e00\u4e2a\u96c6\u7fa4\u5185\u7684\u8d28\u5fc3\u548c\u5404\u6570\u636e\u70b9\u4e4b\u95f4\u8ddd\u79bb\u7684\u5e73\u65b9\u548c\u5f62\u6210\u4e86\u8fd9\u4e2a\u96c6\u7fa4\u7684\u5e73\u65b9\u503c\u4e4b\u548c\u3002\u540c\u65f6\uff0c\u5f53\u6240\u6709\u96c6\u7fa4\u7684\u5e73\u65b9\u503c\u4e4b\u548c\u52a0\u8d77\u6765\u7684\u65f6\u5019\uff0c\u5c31\u7ec4\u6210\u4e86\u96c6\u7fa4\u65b9\u6848\u7684\u5e73\u65b9\u503c\u4e4b\u548c\u3002<\/p>\n<p>\u6211\u4eec\u77e5\u9053\uff0c\u5f53\u96c6\u7fa4\u7684\u6570\u91cf\u589e\u52a0\u65f6\uff0cK\u503c\u4f1a\u6301\u7eed\u4e0b\u964d\u3002\u4f46\u662f\uff0c\u5982\u679c\u4f60\u5c06\u7ed3\u679c\u7528\u56fe\u8868\u6765\u8868\u793a\uff0c\u4f60\u4f1a\u770b\u5230\u8ddd\u79bb\u7684\u5e73\u65b9\u603b\u548c\u5feb\u901f\u51cf\u5c11\u3002\u5230\u67d0\u4e2a\u503c k \u4e4b\u540e\uff0c\u51cf\u5c11\u7684\u901f\u5ea6\u5c31\u5927\u5927\u4e0b\u964d\u4e86\u3002\u5728\u6b64\uff0c\u6211\u4eec\u53ef\u4ee5\u627e\u5230\u96c6\u7fa4\u6570\u91cf\u7684\u6700\u4f18\u503c\u3002<\/p>\n<p><img decoding=\"async\" src=\"data:image\/png;base64,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\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/fhujzoQe7TrlSXGhVbPSbH11K9Q6JTGeibQJFQLIXXumjShibPr1JgSJZ1hLh3aPgiayVFn8mgXY71YiaYGzVWnang\/0?wx_fmt=jpeg\" data-type=\"jpeg\" data-ratio=\"0.5039525691699605\" data-w=\"\" \/><\/p>\n<p><strong>Python\u4ee3\u7801<\/strong><\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"python comments\">#Import Library<br \/>\n<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"python keyword\">from<\/code> <code class=\"python plain\">sklearn.cluster <\/code><code class=\"python keyword\">import<\/code> <code class=\"python plain\">KMeans<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"python comments\">#Assumed you have, X (attributes) for training data set and x_test(attributes) of test_dataset<\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"python comments\"># Create KNeighbors classifier object model <\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"python plain\">k_means <\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">KMeans(n_clusters<\/code><code class=\"python keyword\">=<\/code><code class=\"python value\">3<\/code><code class=\"python plain\">, random_state<\/code><code class=\"python keyword\">=<\/code><code class=\"python value\">0<\/code><code class=\"python plain\">)<\/code><\/div>\n<div class=\"line number8 index7 alt1\"><code class=\"python comments\"># Train the model using the training sets and check score<\/code><\/div>\n<div class=\"line number9 index8 alt2\"><code class=\"python plain\">model.fit(X)<\/code><\/div>\n<div class=\"line number11 index10 alt2\"><code class=\"python comments\">#Predict Output<\/code><\/div>\n<div class=\"line number12 index11 alt1\"><code class=\"python plain\">predicted<\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">model.predict(x_test)<\/code><\/div>\n<\/blockquote>\n<p><strong>8\u3001\u968f\u673a\u68ee\u6797<\/strong><\/p>\n<p>\u968f\u673a\u68ee\u6797\u662f\u8868\u793a\u51b3\u7b56\u6811\u603b\u4f53\u7684\u4e00\u4e2a\u4e13\u6709\u540d\u8bcd\u3002\u5728\u968f\u673a\u68ee\u6797\u7b97\u6cd5\u4e2d\uff0c\u6211\u4eec\u6709\u4e00\u7cfb\u5217\u7684\u51b3\u7b56\u6811\uff08\u56e0\u6b64\u53c8\u540d\u201c\u68ee\u6797\u201d\uff09\u3002\u4e3a\u4e86\u6839\u636e\u4e00\u4e2a\u65b0\u5bf9\u8c61\u7684\u5c5e\u6027\u5c06\u5176\u5206\u7c7b\uff0c\u6bcf\u4e00\u4e2a\u51b3\u7b56\u6811\u6709\u4e00\u4e2a\u5206\u7c7b\uff0c\u79f0\u4e4b\u4e3a\u8fd9\u4e2a\u51b3\u7b56\u6811\u201c\u6295\u7968\u201d\u7ed9\u8be5\u5206\u7c7b\u3002\u8fd9\u4e2a\u68ee\u6797\u9009\u62e9\u83b7\u5f97\u68ee\u6797\u91cc\uff08\u5728\u6240\u6709\u6811\u4e2d\uff09\u83b7\u5f97\u7968\u6570\u6700\u591a\u7684\u5206\u7c7b\u3002<\/p>\n<p>\u6bcf\u68f5\u6811\u662f\u50cf\u8fd9\u6837\u79cd\u690d\u517b\u6210\u7684\uff1a<\/p>\n<blockquote>\n<ol class=\" list-paddingleft-2\">\n<li>\u5982\u679c\u8bad\u7ec3\u96c6\u7684\u6848\u4f8b\u6570\u662f N\uff0c\u5219\u4ece N \u4e2a\u6848\u4f8b\u4e2d\u7528\u91cd\u7f6e\u62bd\u6837\u6cd5\u968f\u673a\u62bd\u53d6\u6837\u672c\u3002\u8fd9\u4e2a\u6837\u672c\u5c06\u4f5c\u4e3a\u201c\u517b\u80b2\u201d\u6811\u7684\u8bad\u7ec3\u96c6\u3002<\/li>\n<li>\u5047\u5982\u6709 M \u4e2a\u8f93\u5165\u53d8\u91cf\uff0c\u5219\u5b9a\u4e49\u4e00\u4e2a\u6570\u5b57 m&lt;&lt;M\u3002m \u8868\u793a\uff0c\u4ece M \u4e2d\u968f\u673a\u9009\u4e2d m \u4e2a\u53d8\u91cf\uff0c\u8fd9 m \u4e2a\u53d8\u91cf\u4e2d\u6700\u597d\u7684\u5207\u5206\u4f1a\u88ab\u7528\u6765\u5207\u5206\u8be5\u8282\u70b9\u3002\u5728\u79cd\u690d\u68ee\u6797\u7684\u8fc7\u7a0b\u4e2d\uff0cm \u7684\u503c\u4fdd\u6301\u4e0d\u53d8\u3002<\/li>\n<li>\u5c3d\u53ef\u80fd\u5927\u5730\u79cd\u690d\u6bcf\u4e00\u68f5\u6811\uff0c\u5168\u7a0b\u4e0d\u526a\u679d\u3002<\/li>\n<\/ol>\n<\/blockquote>\n<p>\u82e5\u60f3\u4e86\u89e3\u8fd9\u4e2a\u7b97\u6cd5\u7684\u66f4\u591a\u7ec6\u8282\uff0c\u6bd4\u8f83\u51b3\u7b56\u6811\u4ee5\u53ca\u4f18\u5316\u6a21\u578b\u53c2\u6570\uff0c\u6211\u5efa\u8bae\u4f60\u9605\u8bfb\u4ee5\u4e0b\u6587\u7ae0\uff1a<\/p>\n<ol class=\" list-paddingleft-2\">\n<li>\u968f\u673a\u68ee\u6797\u5165\u95e8\u2014\u7b80\u5316\u7248<\/li>\n<li>\u5c06 CART \u6a21\u578b\u4e0e\u968f\u673a\u68ee\u6797\u6bd4\u8f83\uff08\u4e0a\uff09<\/li>\n<li>\u5c06\u968f\u673a\u68ee\u6797\u4e0e CART \u6a21\u578b\u6bd4\u8f83\uff08\u4e0b\uff09<\/li>\n<li>\u8c03\u6574\u4f60\u7684\u968f\u673a\u68ee\u6797\u6a21\u578b\u53c2\u6570<\/li>\n<\/ol>\n<p><strong>Python<\/strong><\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"python comments\">#Import Library<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"python keyword\">from<\/code> <code class=\"python plain\">sklearn.ensemble <\/code><code class=\"python keyword\">import<\/code> <code class=\"python plain\">RandomForestClassifier<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"python comments\">#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset<\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"python comments\"># Create Random Forest object<\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"python plain\">model<\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">RandomForestClassifier()<\/code><\/div>\n<div class=\"line number8 index7 alt1\"><code class=\"python comments\"># Train the model using the training sets and check score<\/code><\/div>\n<div class=\"line number9 index8 alt2\"><code class=\"python plain\">model.fit(X, y)<\/code><\/div>\n<div class=\"line number11 index10 alt2\"><code class=\"python comments\">#Predict Output<\/code><\/div>\n<div class=\"line number12 index11 alt1\"><code class=\"python plain\">predicted<\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">model.predict(x_test)<\/code><\/div>\n<\/blockquote>\n<p><strong>R\u4ee3\u7801<\/strong><\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"text plain\">library(randomForest)<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"text plain\">x &lt;- cbind(x_train,y_train)<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"text plain\"># Fitting model<\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"text plain\">fit &lt;- randomForest(Species ~ ., x,ntree=500)<\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"text plain\">summary(fit)<\/code><\/div>\n<div class=\"line number8 index7 alt1\"><code class=\"text plain\">#Predict Output <\/code><\/div>\n<div class=\"line number9 index8 alt2\"><code class=\"text plain\">predicted= predict(fit,x_test)<\/code><\/div>\n<\/blockquote>\n<p><strong>9\u3001\u964d\u7ef4\u7b97\u6cd5<\/strong><\/p>\n<p>\u5728\u8fc7\u53bb\u7684 4 \u5230 5 \u5e74\u91cc\uff0c\u5728\u6bcf\u4e00\u4e2a\u53ef\u80fd\u7684\u9636\u6bb5\uff0c\u4fe1\u606f\u6355\u6349\u90fd\u5448\u6307\u6570\u589e\u957f\u3002\u516c\u53f8\u3001\u653f\u5e9c\u673a\u6784\u3001\u7814\u7a76\u7ec4\u7ec7\u5728\u5e94\u5bf9\u7740\u65b0\u8d44\u6e90\u4ee5\u5916\uff0c\u8fd8\u6355\u6349\u8be6\u5c3d\u7684\u4fe1\u606f\u3002<\/p>\n<p>\u4e3e\u4e2a\u4f8b\u5b50\uff1a\u7535\u5b50\u5546\u52a1\u516c\u53f8\u66f4\u8be6\u7ec6\u5730\u6355\u6349\u5173\u4e8e\u987e\u5ba2\u7684\u8d44\u6599\uff1a\u4e2a\u4eba\u4fe1\u606f\u3001\u7f51\u7edc\u6d4f\u89c8\u8bb0\u5f55\u3001\u4ed6\u4eec\u7684\u559c\u6076\u3001\u8d2d\u4e70\u8bb0\u5f55\u3001\u53cd\u9988\u4ee5\u53ca\u522b\u7684\u8bb8\u591a\u4fe1\u606f\uff0c\u6bd4\u4f60\u8eab\u8fb9\u7684\u6742\u8d27\u5e97\u552e\u8d27\u5458\u66f4\u52a0\u5173\u6ce8\u4f60\u3002<\/p>\n<p>\u4f5c\u4e3a\u4e00\u4e2a\u6570\u636e\u79d1\u5b66\u5bb6\uff0c\u6211\u4eec\u63d0\u4f9b\u7684\u6570\u636e\u5305\u542b\u8bb8\u591a\u7279\u70b9\u3002\u8fd9\u542c\u8d77\u6765\u7ed9\u5efa\u7acb\u4e00\u4e2a\u7ecf\u5f97\u8d77\u8003\u7814\u7684\u6a21\u578b\u63d0\u4f9b\u4e86\u5f88\u597d\u6750\u6599\uff0c\u4f46\u6709\u4e00\u4e2a\u6311\u6218\uff1a\u5982\u4f55\u4ece 1000 \u6216\u8005 2000 \u91cc\u5206\u8fa8\u51fa\u6700\u91cd\u8981\u7684\u53d8\u91cf\u5462\uff1f\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u964d\u7ef4\u7b97\u6cd5\u548c\u522b\u7684\u4e00\u4e9b\u7b97\u6cd5\uff08\u6bd4\u5982\u51b3\u7b56\u6811\u3001\u968f\u673a\u68ee\u6797\u3001PCA\u3001\u56e0\u5b50\u5206\u6790\uff09\u5e2e\u52a9\u6211\u4eec\u6839\u636e\u76f8\u5173\u77e9\u9635\uff0c\u7f3a\u5931\u7684\u503c\u7684\u6bd4\u4f8b\u548c\u522b\u7684\u8981\u7d20\u6765\u627e\u51fa\u8fd9\u4e9b\u91cd\u8981\u53d8\u91cf\u3002<\/p>\n<p>\u60f3\u8981\u77e5\u9053\u66f4\u591a\u5173\u4e8e\u8be5\u7b97\u6cd5\u7684\u4fe1\u606f\uff0c\u53ef\u4ee5\u9605\u8bfb\u300a\u964d\u7ef4\u7b97\u6cd5\u7684\u521d\u5b66\u8005\u6307\u5357\u300b\u3002<\/p>\n<p><strong>Python\u4ee3\u7801<\/strong><\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"python comments\">#Import Library<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"python keyword\">from<\/code> <code class=\"python plain\">sklearn <\/code><code class=\"python keyword\">import<\/code> <code class=\"python plain\">decomposition<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"python comments\">#Assumed you have training and test data set as train and test<\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"python comments\"># Create PCA obeject pca= decomposition.PCA(n_components=k) #default value of k =min(n_sample, n_features)<\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"python comments\"># For Factor analysis<\/code><\/div>\n<div class=\"line number7 index6 alt2\"><code class=\"python comments\">#fa= decomposition.FactorAnalysis()<\/code><\/div>\n<div class=\"line number8 index7 alt1\"><code class=\"python comments\"># Reduced the dimension of training dataset using PCA<\/code><\/div>\n<div class=\"line number9 index8 alt2\"><code class=\"python plain\">train_reduced <\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">pca.fit_transform(train)<\/code><\/div>\n<div class=\"line number11 index10 alt2\"><code class=\"python comments\">#Reduced the dimension of test dataset<\/code><\/div>\n<div class=\"line number12 index11 alt1\"><code class=\"python plain\">test_reduced <\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">pca.transform(test)<\/code><\/div>\n<div class=\"line number14 index13 alt1\"><code class=\"python comments\">#For more detail on this, please refer this link.<\/code><\/div>\n<\/blockquote>\n<p><strong>R Code<\/strong><\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"text plain\">library(stats)<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"text plain\">pca &lt;- princomp(train, cor = TRUE)<\/code><\/div>\n<div class=\"line number3 index2 alt2\"><code class=\"text plain\">train_reduced &lt;- predict(pca,train)<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"text plain\">test_reduced &lt;- predict(pca,test)<\/code><\/div>\n<\/blockquote>\n<p><strong>10\u3001Gradient Boosting \u548c AdaBoost \u7b97\u6cd5<\/strong><\/p>\n<p>\u5f53\u6211\u4eec\u8981\u5904\u7406\u5f88\u591a\u6570\u636e\u6765\u505a\u4e00\u4e2a\u6709\u9ad8\u9884\u6d4b\u80fd\u529b\u7684\u9884\u6d4b\u65f6\uff0c\u6211\u4eec\u4f1a\u7528\u5230 GBM \u548c AdaBoost \u8fd9\u4e24\u79cd boosting \u7b97\u6cd5\u3002boosting \u7b97\u6cd5\u662f\u4e00\u79cd\u96c6\u6210\u5b66\u4e60\u7b97\u6cd5\u3002\u5b83\u7ed3\u5408\u4e86\u5efa\u7acb\u5728\u591a\u4e2a\u57fa\u7840\u4f30\u8ba1\u503c\u57fa\u7840\u4e0a\u7684\u9884\u6d4b\u7ed3\u679c\uff0c\u6765\u589e\u8fdb\u5355\u4e2a\u4f30\u8ba1\u503c\u7684\u53ef\u9760\u7a0b\u5ea6\u3002\u8fd9\u4e9b boosting \u7b97\u6cd5\u901a\u5e38\u5728\u6570\u636e\u79d1\u5b66\u6bd4\u8d5b\u5982 Kaggl\u3001AV Hackathon\u3001CrowdAnalytix \u4e2d\u5f88\u6709\u6548\u3002<\/p>\n<p>\u66f4\u591a\uff1a\u8be6\u5c3d\u4e86\u89e3 Gradient \u548c AdaBoost<\/p>\n<h4>Python\u4ee3\u7801<\/h4>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"python comments\">#Import Library<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"python keyword\">from<\/code> <code class=\"python plain\">sklearn.ensemble <\/code><code class=\"python keyword\">import<\/code> <code class=\"python plain\">GradientBoostingClassifier<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"python comments\">#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset<\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"python comments\"># Create Gradient Boosting Classifier object<\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"python plain\">model<\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">GradientBoostingClassifier(n_estimators<\/code><code class=\"python keyword\">=<\/code><code class=\"python value\">100<\/code><code class=\"python plain\">, learning_rate<\/code><code class=\"python keyword\">=<\/code><code class=\"python value\">1.0<\/code><code class=\"python plain\">, max_depth<\/code><code class=\"python keyword\">=<\/code><code class=\"python value\">1<\/code><code class=\"python plain\">, random_state<\/code><code class=\"python keyword\">=<\/code><code class=\"python value\">0<\/code><code class=\"python plain\">)<\/code><\/div>\n<div class=\"line number8 index7 alt1\"><code class=\"python comments\"># Train the model using the training sets and check score<\/code><\/div>\n<div class=\"line number9 index8 alt2\"><code class=\"python plain\">model.fit(X, y)<\/code><\/div>\n<div class=\"line number11 index10 alt2\"><code class=\"python comments\">#Predict Output<\/code><\/div>\n<div class=\"line number12 index11 alt1\"><code class=\"python plain\">predicted<\/code><code class=\"python keyword\">=<\/code> <code class=\"python plain\">model.predict(x_test)<\/code><\/div>\n<\/blockquote>\n<p><strong>R code<\/strong><\/p>\n<blockquote>\n<div class=\"line number1 index0 alt2\"><code class=\"text plain\">library(caret)<\/code><\/div>\n<div class=\"line number2 index1 alt1\"><code class=\"text plain\">x &lt;- cbind(x_train,y_train)<\/code><\/div>\n<div class=\"line number4 index3 alt1\"><code class=\"text plain\"># Fitting model<\/code><\/div>\n<div class=\"line number5 index4 alt2\"><code class=\"text plain\">fitControl &lt;- trainControl( method = \"repeatedcv\", number = 4, repeats = 4)<\/code><\/div>\n<div class=\"line number6 index5 alt1\"><code class=\"text plain\">fit &lt;- train(y ~ ., data = x, method = \"gbm\", trControl = fitControl,verbose = FALSE)<\/code><\/div>\n<div class=\"line number7 index6 alt2\"><code class=\"text plain\">predicted= predict(fit,x_test,type= \"prob\")[,2]<\/code><\/div>\n<\/blockquote>\n<p>\u7ed3\u8bedGradientBoostingClassifier \u548c\u968f\u673a\u68ee\u6797\u662f\u4e24\u79cd\u4e0d\u540c\u7684 boosting \u6811\u5206\u7c7b\u5668\u3002\u4eba\u4eec\u5e38\u5e38\u95ee\u8d77\u8fd9\u4e24\u4e2a\u7b97\u6cd5\u4e4b\u95f4\u7684\u533a\u522b\u3002<\/p>\n<p>\u73b0\u5728\u6211\u80fd\u786e\u5b9a\uff0c\u4f60\u5bf9\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u5e94\u8be5\u6709\u4e86\u5927\u81f4\u7684\u4e86\u89e3\u3002\u5199\u8fd9\u7bc7\u6587\u7ae0\u5e76\u63d0\u4f9b Python \u548c R \u8bed\u8a00\u4ee3\u7801\u7684\u552f\u4e00\u76ee\u7684\uff0c\u5c31\u662f\u8ba9\u4f60\u7acb\u9a6c\u5f00\u59cb\u5b66\u4e60\u3002<\/p>\n<p>\u5982\u679c\u4f60\u60f3\u8981\u638c\u63e1\u673a\u5668\u5b66\u4e60\uff0c\u90a3\u5c31\u7acb\u523b\u5f00\u59cb\u5427\u3002\u505a\u505a\u7ec3\u4e60\uff0c\u7406\u6027\u5730\u8ba4\u8bc6\u6574\u4e2a\u8fc7\u7a0b\uff0c\u5e94\u7528\u8fd9\u4e9b\u4ee3\u7801\uff0c\u5e76\u611f\u53d7\u4e50\u8da3\u5427\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"<p>2015-10-24 \u4f2f\u4e50\u5728\u7ebf \u7a0b\u5e8f\u5458\u7684\u90a3\u4e9b\u4e8b \u524d\u8a00 \u8c37\u6b4c\u8463\u4e8b\u957f\u65bd\u5bc6\u7279\u66fe\u8bf4\u8fc7\uff1a\u867d\u7136\u8c37\u6b4c\u7684\u65e0\u4eba\u9a7e\u9a76\u6c7d\u8f66\u548c\u673a\u5668\u4eba\u53d7 &hellip; <a href=\"http:\/\/blog.softwareclues.com\/zh\/10-%e7%a7%8d%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e7%ae%97%e6%b3%95%e7%9a%84%e8%a6%81%e7%82%b9\" class=\"more-link\">\u7ee7\u7eed\u9605\u8bfb<span class=\"screen-reader-text\">\u201c10 \u79cd\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u7684\u8981\u70b9\u201d<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[3],"tags":[57,56],"translation":{"provider":"WPGlobus","version":"2.12.2","language":"zh","enabled_languages":["en","zh"],"languages":{"en":{"title":true,"content":true,"excerpt":false},"zh":{"title":false,"content":false,"excerpt":false}}},"jetpack_publicize_connections":[],"yoast_head":"<!-- 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