{"id":85,"date":"2016-04-30T22:57:10","date_gmt":"2016-04-30T22:57:10","guid":{"rendered":"http:\/\/blogs.softwareclue.com\/?p=85"},"modified":"2016-05-07T21:38:50","modified_gmt":"2016-05-07T21:38:50","slug":"common-machine-learning-algorithms","status":"publish","type":"post","link":"http:\/\/blog.softwareclues.com\/zh\/common-machine-learning-algorithms","title":{"rendered":"\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u4e00\u89c8\uff08\u9644python\u548cR\u4ee3\u7801\uff09"},"content":{"rendered":"<p><em id=\"post-date\" class=\"rich_media_meta rich_media_meta_text\">2016-04-19<\/em> <span class=\"rich_media_meta rich_media_meta_text rich_media_meta_nickname\">\u5927\u6570\u636e\u6587\u6458<\/span><br \/>\n\u7f16\u8bd1\uff1a@\u9152\u9152<br 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target=\"_blank\">\u968f\u673a\u68ee\u6797<\/a>\uff0cK\u90bb\u8fd1\u7b97\u6cd5\uff0c\u903b\u8f91\u56de\u5f52\u7b49\u3002<\/p>\n<h3><\/h3>\n<h3>2. \u65e0\u76d1\u7763\u5f0f\u5b66\u4e60<\/h3>\n<p>\u4e0e\u76d1\u7763\u5f0f\u5b66\u4e60\u4e0d\u540c\u7684\u662f\uff0c\u65e0\u76d1\u7763\u5b66\u4e60\u4e2d\u6211\u4eec\u6ca1\u6709\u9700\u8981\u9884\u6d4b\u6216\u4f30\u8ba1\u7684\u76ee\u6807\u53d8\u91cf\u3002\u65e0\u76d1\u7763\u5f0f\u5b66\u4e60\u662f\u7528\u6765\u5bf9\u603b\u4f53\u5bf9\u8c61\u8fdb\u884c\u5206\u7c7b\u7684\u3002\u5b83\u5728\u6839\u636e\u67d0\u4e00\u6307\u6807\u5c06\u5ba2\u6237\u5206\u7c7b\u4e0a\u6709\u5e7f\u6cdb\u5e94\u7528\u3002<br \/>\n\u5c5e\u4e8e\u65e0\u76d1\u7763\u5f0f\u5b66\u4e60\u7684\u7b97\u6cd5\u6709\uff1a\u5173\u8054\u89c4\u5219\uff0cK-means\u805a\u7c7b\u7b97\u6cd5\u7b49\u3002<\/p>\n<h3><\/h3>\n<h3>3. \u5f3a\u5316\u5b66\u4e60<\/h3>\n<p>\u8fd9\u4e2a\u7b97\u6cd5\u53ef\u4ee5\u8bad\u7ec3\u7a0b\u5e8f\u505a\u51fa\u67d0\u4e00\u51b3\u5b9a\u3002\u7a0b\u5e8f\u5728\u67d0\u4e00\u60c5\u51b5\u4e0b\u5c1d\u8bd5\u6240\u6709\u7684\u53ef\u80fd\u884c\u52a8\uff0c\u8bb0\u5f55\u4e0d\u540c\u884c\u52a8\u7684\u7ed3\u679c\u5e76\u8bd5\u7740\u627e\u51fa\u6700\u597d\u7684\u4e00\u6b21\u5c1d\u8bd5\u6765\u505a\u51b3\u5b9a\u3002<br \/>\n\u5c5e\u4e8e\u8fd9\u4e00\u7c7b\u7b97\u6cd5\u7684\u6709\u9a6c\u5c14\u53ef\u592b\u51b3\u7b56\u8fc7\u7a0b\u3002<\/p>\n<h2><\/h2>\n<h2>\u5e38\u89c1\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5<\/h2>\n<p>\u4ee5\u4e0b\u662f\u6700\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u5927\u90e8\u5206\u6570\u636e\u95ee\u9898\u90fd\u53ef\u4ee5\u901a\u8fc7\u5b83\u4eec\u89e3\u51b3\uff1a<\/p>\n<p><code>1.\u7ebf\u6027\u56de\u5f52 (Linear Regression) 2.\u903b\u8f91\u56de\u5f52 (Logistic Regression) 3.\u51b3\u7b56\u6811 (Decision Tree) 4.\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09 5.\u6734\u7d20\u8d1d\u53f6\u65af (Naive Bayes) 6.K\u90bb\u8fd1\u7b97\u6cd5\uff08KNN\uff09 7.K-\u5747\u503c\u7b97\u6cd5\uff08K-means\uff09 8.\u968f\u673a\u68ee\u6797 (Random Forest) 9.\u964d\u4f4e\u7ef4\u5ea6\u7b97\u6cd5\uff08Dimensionality Reduction Algorithms\uff09 10.Gradient Boost\u548cAdaboost\u7b97\u6cd5<\/code><\/p>\n<h3>1.\u7ebf\u6027\u56de\u5f52 (Linear Regression)<\/h3>\n<p>\u7ebf\u6027\u56de\u5f52\u662f\u5229\u7528\u8fde\u7eed\u6027\u53d8\u91cf\u6765\u4f30\u8ba1\u5b9e\u9645\u6570\u503c\uff08\u4f8b\u5982\u623f\u4ef7\uff0c\u547c\u53eb\u6b21\u6570\u548c\u603b\u9500\u552e\u989d\u7b49\uff09\u3002\u6211\u4eec\u901a\u8fc7\u7ebf\u6027\u56de\u5f52\u7b97\u6cd5\u627e\u51fa\u81ea\u53d8\u91cf\u548c\u56e0\u53d8\u91cf\u95f4\u7684\u6700\u4f73\u7ebf\u6027\u5173\u7cfb\uff0c\u56fe\u5f62\u4e0a\u53ef\u4ee5\u786e\u5b9a\u4e00\u6761\u6700\u4f73\u76f4\u7ebf\u3002\u8fd9\u6761\u6700\u4f73\u76f4\u7ebf\u5c31\u662f\u56de\u5f52\u7ebf\u3002\u8fd9\u4e2a\u56de\u5f52\u5173\u7cfb\u53ef\u4ee5\u7528Y=aX+b 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list-paddingleft-2\">\n<li>Y- \u56e0\u53d8\u91cf<\/li>\n<li>a- \u659c\u7387<\/li>\n<li>X- \u81ea\u53d8\u91cf<\/li>\n<li>b- \u622a\u8ddd<\/li>\n<\/ul>\n<p>a\u548cb\u53ef\u4ee5\u901a\u8fc7\u6700\u5c0f\u5316\u56e0\u53d8\u91cf\u8bef\u5dee\u7684\u5e73\u65b9\u548c\u5f97\u5230\uff08\u6700\u5c0f\u4e8c\u4e58\u6cd5\uff09\u3002<\/p>\n<p>\u4e0b\u56fe\u4e2d\u6211\u4eec\u5f97\u5230\u7684\u7ebf\u6027\u56de\u5f52\u65b9\u7a0b\u662f y=0.2811X+13.9\u3002\u901a\u8fc7\u8fd9\u4e2a\u65b9\u7a0b\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u4e00\u4e2a\u4eba\u7684\u8eab\u9ad8\u5f97\u5230\u4ed6\u7684\u4f53\u91cd\u4fe1\u606f\u3002<\/p>\n<p><img decoding=\"async\" class=\"\" title=\"\" src=\"data:image\/png;base64,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\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjtlOCkwYL1FMMciaGoOIYPFhbhknXD6vUQZlC0vclADwZ0ic2dHVKYSGCQ\/0?wx_fmt=png\" data-type=\"png\" data-ratio=\"0.5945378151260504\" data-w=\"476\" \/><\/p>\n<p>\u7ebf\u6027\u56de\u5f52\u4e3b\u8981\u6709\u4e24\u79cd\uff1a\u4e00\u5143\u7ebf\u6027\u56de\u5f52\u548c\u591a\u5143\u7ebf\u6027\u56de\u5f52\u3002\u4e00\u5143\u7ebf\u6027\u56de\u5f52\u53ea\u6709\u4e00\u4e2a\u81ea\u53d8\u91cf\uff0c\u800c\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6709\u591a\u4e2a\u81ea\u53d8\u91cf\u3002\u62df\u5408\u591a\u5143\u7ebf\u6027\u56de\u5f52\u7684\u65f6\u5019\uff0c\u53ef\u4ee5\u5229\u7528\u591a\u9879\u5f0f\u56de\u5f52\uff08Polynomial Regression\uff09\u6216\u66f2\u7ebf\u56de\u5f52 (Curvilinear Regression)\u3002<\/p>\n<p><em><strong>Python \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-python\">#Import Library<\/code><\/li>\n<li><code class=\"language-python\">#Import other necessary libraries like pandas, numpy...<\/code><\/li>\n<li><code class=\"language-python\"><span class=\"kwd\">from<\/span><span class=\"pln\"> sklearn <\/span><span class=\"kwd\">import<\/span><span class=\"pln\"> linear_model<\/span><\/code><\/li>\n<li><code class=\"language-python\">#Load Train and Test datasets<\/code><\/li>\n<li><code class=\"language-python\">#Identify feature and response variable(s) and values must be numeric and numpy arrays<\/code><\/li>\n<li><code class=\"language-python\"><span class=\"pln\">x_train<\/span><span class=\"pun\">=<\/span><span class=\"pln\">input_variables_values_training_datasets<\/span><\/code><\/li>\n<li><code class=\"language-python\"><span class=\"pln\">y_train<\/span><span class=\"pun\">=<\/span><span class=\"pln\">target_variables_values_training_datasets<\/span><\/code><\/li>\n<li><code class=\"language-python\"><span class=\"pln\">x_test<\/span><span class=\"pun\">=<\/span><span class=\"pln\">input_variables_values_test_datasets<\/span><\/code><\/li>\n<li><code class=\"language-python\"># Create linear regression object<\/code><\/li>\n<li><code class=\"language-python\"><span class=\"pln\">linear <\/span><span class=\"pun\">=<\/span><span class=\"pln\"> linear_model<\/span><span class=\"pun\">.<\/span><span class=\"typ\">LinearRegression<\/span><span class=\"pun\">()<\/span><\/code><\/li>\n<li><code class=\"language-python\"># Train the model using the training sets and check score<\/code><\/li>\n<li><code class=\"language-python\"><span class=\"pln\">linear<\/span><span class=\"pun\">.<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_train<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> y_train<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-python\"><span class=\"pln\">linear<\/span><span class=\"pun\">.<\/span><span class=\"pln\">score<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_train<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> y_train<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-python\">#Equation coefficient and Intercept<\/code><\/li>\n<li><code class=\"language-python\"><span class=\"kwd\">print<\/span><span class=\"pun\">(<\/span><span class=\"str\">'Coefficient: \\n'<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> linear<\/span><span class=\"pun\">.<\/span><span class=\"pln\">coef_<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-python\"><span class=\"kwd\">print<\/span><span class=\"pun\">(<\/span><span class=\"str\">'Intercept: \\n'<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> linear<\/span><span class=\"pun\">.<\/span><span class=\"pln\">intercept_<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-python\">#Predict Output<\/code><\/li>\n<li><code class=\"language-python\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> linear<\/span><span class=\"pun\">.<\/span><span class=\"pln\">predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<p><em><strong>R \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-R\">#Load Train and Test datasets<\/code><\/li>\n<li><code class=\"language-R\">#Identify feature and response variable(s) and values must be numeric and numpy arrays<\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">x_train <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> input_variables_values_training_datasets<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">y_train <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> target_variables_values_training_datasets<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">x_test <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> input_variables_values_test_datasets<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">x <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> cbind<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_train<\/span><span class=\"pun\">,<\/span><span class=\"pln\">y_train<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"># Train the model using the training sets and check score<\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">linear <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> lm<\/span><span class=\"pun\">(<\/span><span class=\"pln\">y_train <\/span><span class=\"pun\">~<\/span> <span class=\"pun\">.,<\/span><span class=\"pln\"> data <\/span><span class=\"pun\">=<\/span><span class=\"pln\"> x<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">summary<\/span><span class=\"pun\">(<\/span><span class=\"pln\">linear<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\">#Predict Output<\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">linear<\/span><span class=\"pun\">,<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">)<\/span> <\/code><\/li>\n<\/ol>\n<h3><\/h3>\n<h3>2.\u903b\u8f91\u56de\u5f52<\/h3>\n<p>\u522b\u88ab\u5b83\u7684\u540d\u5b57\u8ff7\u60d1\u4e86\uff0c\u903b\u8f91\u56de\u5f52\u5176\u5b9e\u662f\u4e00\u4e2a\u5206\u7c7b\u7b97\u6cd5\u800c\u4e0d\u662f\u56de\u5f52\u7b97\u6cd5\u3002\u901a\u5e38\u662f\u5229\u7528\u5df2\u77e5\u7684\u81ea\u53d8\u91cf\u6765\u9884\u6d4b\u4e00\u4e2a\u79bb\u6563\u578b\u56e0\u53d8\u91cf\u7684\u503c\uff08\u50cf\u4e8c\u8fdb\u5236\u503c0\/1\uff0c\u662f\/\u5426\uff0c\u771f\/\u5047\uff09\u3002\u7b80\u5355\u6765\u8bf4\uff0c\u5b83\u5c31\u662f\u901a\u8fc7\u62df\u5408\u4e00\u4e2a\u903b\u8f91\u51fd\u6570\uff08<a target=\"_blank\">logit fuction<\/a>\uff09\u6765\u9884\u6d4b\u4e00\u4e2a\u4e8b\u4ef6\u53d1\u751f\u7684\u6982\u7387\u3002\u6240\u4ee5\u5b83\u9884\u6d4b\u7684\u662f\u4e00\u4e2a\u6982\u7387\u503c\uff0c\u81ea\u7136\uff0c\u5b83\u7684\u8f93\u51fa\u503c\u5e94\u8be5\u57280\u52301\u4e4b\u95f4\u3002<\/p>\n<p>\u540c\u6837\uff0c\u6211\u4eec\u53ef\u4ee5\u7528\u4e00\u4e2a\u4f8b\u5b50\u6765\u7406\u89e3\u8fd9\u4e2a\u7b97\u6cd5\u3002<\/p>\n<p>\u5047\u8bbe\u4f60\u7684\u4e00\u4e2a\u670b\u53cb\u8ba9\u4f60\u56de\u7b54\u4e00\u9053\u9898\u3002\u53ef\u80fd\u7684\u7ed3\u679c\u53ea\u6709\u4e24\u79cd\uff1a\u4f60\u7b54\u5bf9\u4e86\u6216\u6ca1\u6709\u7b54\u5bf9\u3002\u4e3a\u4e86\u7814\u7a76\u4f60\u6700\u64c5\u957f\u7684\u9898\u76ee\u9886\u57df\uff0c\u4f60\u505a\u4e86\u5404\u79cd\u9886\u57df\u7684\u9898\u76ee\u3002\u90a3\u4e48\u8fd9\u4e2a\u7814\u7a76\u7684\u7ed3\u679c\u53ef\u80fd\u662f\u8fd9\u6837\u7684\uff1a\u5982\u679c\u662f\u4e00\u9053\u5341\u5e74\u7ea7\u7684\u4e09\u89d2\u51fd\u6570\u9898\uff0c\u4f60\u670970%\u7684\u53ef\u80fd\u6027\u80fd\u89e3\u51fa\u5b83\u3002\u4f46\u5982\u679c\u662f\u4e00\u9053\u4e94\u5e74\u7ea7\u7684\u5386\u53f2\u9898\uff0c\u4f60\u4f1a\u7684\u6982\u7387\u53ef\u80fd\u53ea\u670930%\u3002\u903b\u8f91\u56de\u5f52\u5c31\u662f\u7ed9\u4f60\u8fd9\u6837\u7684\u6982\u7387\u7ed3\u679c\u3002<\/p>\n<p>\u56de\u5230\u6570\u5b66\u4e0a\uff0c\u4e8b\u4ef6\u7ed3\u679c\u7684\u80dc\u7b97\u5bf9\u6570\uff08log odds\uff09\u53ef\u4ee5\u7528\u9884\u6d4b\u53d8\u91cf\u7684\u7ebf\u6027\u7ec4\u5408\u6765\u63cf\u8ff0\uff1a<\/p>\n<p><code>odds= p\/ (1-p) = probability of event occurrence \/ probability of not event occurrence ln(odds) = ln(p\/(1-p)) logit(p) = ln(p\/(1-p)) = b0+b1X1+b2X2+b3X3....+bkXk<\/code><\/p>\n<p>\u5728\u8fd9\u91cc\uff0cp \u662f\u6211\u4eec\u611f\u5174\u8da3\u7684\u4e8b\u4ef6\u51fa\u73b0\u7684\u6982\u7387\u3002\u5b83\u901a\u8fc7\u7b5b\u9009\u51fa\u7279\u5b9a\u53c2\u6570\u503c\u4f7f\u5f97\u89c2\u5bdf\u5230\u7684\u6837\u672c\u503c\u51fa\u73b0\u7684\u6982\u7387\u6700\u5927\u5316\uff0c\u6765\u4f30\u8ba1\u53c2\u6570\uff0c\u800c\u4e0d\u662f\u50cf\u666e\u901a\u56de\u5f52\u90a3\u6837\u6700\u5c0f\u5316\u8bef\u5dee\u7684\u5e73\u65b9\u548c\u3002<\/p>\n<p>\u4f60\u53ef\u80fd\u4f1a\u95ee\u4e3a\u4ec0\u4e48\u9700\u8981\u505a\u5bf9\u6570\u5462\uff1f\u7b80\u5355\u6765\u8bf4\u8fd9\u662f\u91cd\u590d\u9636\u68af\u51fd\u6570\u7684\u6700\u4f73\u65b9\u6cd5\u3002\u56e0\u672c\u7bc7\u6587\u7ae0\u65e8\u4e0d\u5728\u6b64\uff0c\u8fd9\u65b9\u9762\u5c31\u4e0d\u505a\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u3002<\/p>\n<p><img decoding=\"async\" class=\"\" title=\"\" src=\"data:image\/png;base64,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\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjtHbA0OzRdaSadag5ic4b7KPJFBLGnMWpVcbMDULpiaRCJDswbQTewQh9g\/0?wx_fmt=png\" data-type=\"png\" data-ratio=\"0.7617260787992496\" data-w=\"533\" \/><\/p>\n<p><em><strong>Python \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-Python\">#Import Library<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"kwd\">from<\/span><span class=\"pln\"> sklearn<\/span><span class=\"pun\">.<\/span><span class=\"pln\">linear_model <\/span><span class=\"kwd\">import<\/span> <span class=\"typ\">LogisticRegression<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset<\/code><\/li>\n<li><code class=\"language-Python\"># Create logistic regression object<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">model <\/span><span class=\"pun\">=<\/span> <span class=\"typ\">LogisticRegression<\/span><span class=\"pun\">()<\/span><\/code><\/li>\n<li><code class=\"language-Python\"># Train the model using the training sets and check score<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">(<\/span><span class=\"pln\">X<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> y<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">score<\/span><span class=\"pun\">(<\/span><span class=\"pln\">X<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> y<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Equation coefficient and Intercept<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"kwd\">print<\/span><span class=\"pun\">(<\/span><span class=\"str\">'Coefficient: \\n'<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">coef_<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"kwd\">print<\/span><span class=\"pun\">(<\/span><span class=\"str\">'Intercept: \\n'<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">intercept_<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Predict Output<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<p><em><strong>R \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-R\"><span class=\"pln\">x <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> cbind<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_train<\/span><span class=\"pun\">,<\/span><span class=\"pln\">y_train<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"># Train the model using the training sets and check score<\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">logistic <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> glm<\/span><span class=\"pun\">(<\/span><span class=\"pln\">y_train <\/span><span class=\"pun\">~<\/span> <span class=\"pun\">.,<\/span><span class=\"pln\"> data <\/span><span class=\"pun\">=<\/span><span class=\"pln\"> x<\/span><span class=\"pun\">,<\/span><span class=\"pln\">family<\/span><span class=\"pun\">=<\/span><span class=\"str\">'binomial'<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">summary<\/span><span class=\"pun\">(<\/span><span class=\"pln\">logistic<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\">#Predict Output<\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">logistic<\/span><span class=\"pun\">,<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<p><strong>\u5ef6\u4f38<\/strong>\uff1a<br \/>\n\u4ee5\u4e0b\u662f\u4e00\u4e9b\u53ef\u4ee5\u5c1d\u8bd5\u7684\u4f18\u5316\u6a21\u578b\u7684\u65b9\u6cd5\uff1a<\/p>\n<ul class=\" list-paddingleft-2\">\n<li>\u52a0\u5165\u4ea4\u4e92\u9879\uff08interaction\uff09<\/li>\n<li>\u51cf\u5c11\u7279\u5f81\u53d8\u91cf<\/li>\n<li>\u6b63\u5219\u5316\uff08<a target=\"_blank\">regularization<\/a>\uff09<\/li>\n<li>\u4f7f\u7528\u975e\u7ebf\u6027\u6a21\u578b<\/li>\n<\/ul>\n<h3><\/h3>\n<h3>3.\u51b3\u7b56\u6811<\/h3>\n<p>\u8fd9\u662f\u6211\u6700\u559c\u6b22\u4e5f\u662f\u80fd\u7ecf\u5e38\u4f7f\u7528\u5230\u7684\u7b97\u6cd5\u3002\u5b83\u5c5e\u4e8e\u76d1\u7763\u5f0f\u5b66\u4e60\uff0c\u5e38\u7528\u6765\u89e3\u51b3\u5206\u7c7b\u95ee\u9898\u3002\u4ee4\u4eba\u60ca\u8bb6\u7684\u662f\uff0c\u5b83\u65e2\u53ef\u4ee5\u8fd0\u7528\u4e8e\u7c7b\u522b\u53d8\u91cf\uff08categorical variables\uff09\u4e5f\u53ef\u4ee5\u4f5c\u7528\u4e8e\u8fde\u7eed\u53d8\u91cf\u3002\u8fd9\u4e2a\u7b97\u6cd5\u53ef\u4ee5\u8ba9\u6211\u4eec\u628a\u4e00\u4e2a\u603b\u4f53\u5206\u4e3a\u4e24\u4e2a\u6216\u591a\u4e2a\u7fa4\u7ec4\u3002\u5206\u7ec4\u6839\u636e\u80fd\u591f\u533a\u5206\u603b\u4f53\u7684\u6700\u91cd\u8981\u7684\u7279\u5f81\u53d8\u91cf\/\u81ea\u53d8\u91cf\u8fdb\u884c\u3002\u66f4\u8be6\u7ec6\u7684\u5185\u5bb9\u53ef\u4ee5\u9605\u8bfb\u8fd9\u7bc7\u6587\u7ae0<a target=\"_blank\">Decision Tree Simplified<\/a>\u3002<\/p>\n<p><img decoding=\"async\" class=\"\" title=\"\" src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjtkpjBHljOAVGIhs8HpXz6X2FGKJeufQVkEibJLebdqjLX9N9PN9hia6sA\/640?wx_fmt=png&amp;wxfrom=5&amp;wx_lazy=1\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjtkpjBHljOAVGIhs8HpXz6X2FGKJeufQVkEibJLebdqjLX9N9PN9hia6sA\/0?wx_fmt=png\" data-type=\"png\" data-ratio=\"0.7356115107913669\" data-w=\"\" \/><\/p>\n<p>\u4ece\u4e0a\u56fe\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\uff0c\u603b\u4f53\u4eba\u7fa4\u6700\u7ec8\u5728\u73a9\u4e0e\u5426\u7684\u4e8b\u4ef6\u4e0a\u88ab\u5206\u6210\u4e86\u56db\u4e2a\u7fa4\u7ec4\u3002\u800c\u5206\u7ec4\u662f\u4f9d\u636e\u4e00\u4e9b\u7279\u5f81\u53d8\u91cf\u5b9e\u73b0\u7684\u3002\u7528\u6765\u5206\u7ec4\u7684\u5177\u4f53\u6307\u6807\u6709\u5f88\u591a\uff0c\u6bd4\u5982Gini\uff0cinformation Gain, Chi-square,entropy\u3002<\/p>\n<p>\u7406\u89e3\u51b3\u7b56\u6811\u539f\u7406\u7684\u6700\u597d\u7684\u529e\u6cd5\u5c31\u662f\u73a9Jezzball\u6e38\u620f\u3002\u8fd9\u662f\u5fae\u8f6f\u7684\u4e00\u6b3e\u7ecf\u5178\u6e38\u620f\uff08\u89c1\u4e0b\u56fe\uff09\u3002\u8fd9\u4e2a\u6e38\u620f\u7684\u6700\u7ec8\u4efb\u52a1\u662f\u5728\u4e00\u4e2a\u6709\u79fb\u52a8\u5899\u58c1\u7684\u623f\u95f4\u91cc\uff0c\u901a\u8fc7\u5efa\u9020\u5899\u58c1\u6765\u5c3d\u53ef\u80fd\u5730\u5c06\u623f\u95f4\u5206\u6210\u5c3d\u91cf\u5927\u7684\uff0c\u6ca1\u6709\u5c0f\u7403\u7684\u7a7a\u95f4\u3002<\/p>\n<p><img decoding=\"async\" class=\"\" title=\"\" src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjtECVkDDYFnVibSvjvK7cgRgbkn0ibsWiaOyKPXiaCvKxDmNibRRBuHZQVlUw\/640?wx_fmt=jpeg&amp;wxfrom=5&amp;wx_lazy=1\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjtECVkDDYFnVibSvjvK7cgRgbkn0ibsWiaOyKPXiaCvKxDmNibRRBuHZQVlUw\/0?wx_fmt=jpeg\" data-type=\"jpeg\" data-ratio=\"0.6\" data-w=\"290\" \/><\/p>\n<p>\u6bcf\u4e00\u6b21\u4f60\u7528\u5efa\u5899\u6765\u5206\u5272\u623f\u95f4\uff0c\u5176\u5b9e\u5c31\u662f\u5728\u5c06\u4e00\u4e2a\u603b\u4f53\u5206\u6210\u4e24\u90e8\u5206\u3002\u51b3\u7b56\u6811\u4e5f\u662f\u7528\u7c7b\u4f3c\u65b9\u6cd5\u5c06\u603b\u4f53\u5206\u6210\u5c3d\u91cf\u591a\u7684\u4e0d\u540c\u7ec4\u522b\u3002<\/p>\n<p><strong>\u5ef6\u4f38\u9605\u8bfb<\/strong>\uff1aSimplified Version of Decision Tree Algorithms<\/p>\n<p><em><strong>Python \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-Python\">#Import Library<\/code><\/li>\n<li><code class=\"language-Python\">#Import other necessary libraries like pandas, numpy...<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"kwd\">from<\/span><span class=\"pln\"> sklearn <\/span><span class=\"kwd\">import<\/span><span class=\"pln\"> tree<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset<\/code><\/li>\n<li><code class=\"language-Python\"># Create tree object <\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">model <\/span><span class=\"pun\">=<\/span><span class=\"pln\"> tree<\/span><span class=\"pun\">.<\/span><span class=\"typ\">DecisionTreeClassifier<\/span><span class=\"pun\">(<\/span><span class=\"pln\">criterion<\/span><span class=\"pun\">=<\/span><span class=\"str\">'gini'<\/span><span class=\"pun\">)<\/span> <span class=\"com\"># for classification, here you can change the algorithm as gini or entropy (information gain) by default it is gini \u00a0<\/span><\/code><\/li>\n<li><code class=\"language-Python\"># model = tree.DecisionTreeRegressor() for regression<\/code><\/li>\n<li><code class=\"language-Python\"># Train the model using the training sets and check score<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">(<\/span><span class=\"pln\">X<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> y<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">score<\/span><span class=\"pun\">(<\/span><span class=\"pln\">X<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> y<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Predict Output<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<p><em><strong>R \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-R\"><span class=\"pln\">library<\/span><span class=\"pun\">(<\/span><span class=\"pln\">rpart<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">x <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> cbind<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_train<\/span><span class=\"pun\">,<\/span><span class=\"pln\">y_train<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"># grow tree <\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">fit <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> rpart<\/span><span class=\"pun\">(<\/span><span class=\"pln\">y_train <\/span><span class=\"pun\">~<\/span> <span class=\"pun\">.,<\/span><span class=\"pln\"> data <\/span><span class=\"pun\">=<\/span><span class=\"pln\"> x<\/span><span class=\"pun\">,<\/span><span class=\"pln\">method<\/span><span class=\"pun\">=<\/span><span class=\"str\">\"class\"<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">summary<\/span><span class=\"pun\">(<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\">#Predict Output <\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">,<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<h3><\/h3>\n<h3>4. \u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09<\/h3>\n<p>\u8fd9\u662f\u4e00\u4e2a\u5206\u7c7b\u7b97\u6cd5\u3002\u5728\u8fd9\u4e2a\u7b97\u6cd5\u4e2d\u6211\u4eec\u5c06\u6bcf\u4e00\u4e2a\u6570\u636e\u4f5c\u4e3a\u4e00\u4e2a\u70b9\u5728\u4e00\u4e2an\u7ef4\u7a7a\u95f4\u4e0a\u4f5c\u56fe\uff08n\u662f\u7279\u5f81\u6570\uff09\uff0c\u6bcf\u4e00\u4e2a\u7279\u5f81\u503c\u5c31\u4ee3\u8868\u5bf9\u5e94\u5750\u6807\u503c\u7684\u5927\u5c0f\u3002\u6bd4\u5982\u8bf4\u6211\u4eec\u6709\u4e24\u4e2a\u7279\u5f81\uff1a\u4e00\u4e2a\u4eba\u7684\u8eab\u9ad8\u548c\u53d1\u957f\u3002\u6211\u4eec\u53ef\u4ee5\u5c06\u8fd9\u4e24\u4e2a\u53d8\u91cf\u5728\u4e00\u4e2a\u4e8c\u7ef4\u7a7a\u95f4\u4e0a\u4f5c\u56fe\uff0c\u56fe\u4e0a\u7684\u6bcf\u4e2a\u70b9\u90fd\u6709\u4e24\u4e2a\u5750\u6807\u503c\uff08\u8fd9\u4e9b\u5750\u6807\u8f74\u4e5f\u53eb\u505a\u652f\u6301\u5411\u91cf\uff09\u3002<\/p>\n<p><img decoding=\"async\" class=\"\" title=\"\" src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjt15WMXNepUAeNfvVicZHdXJcZWT4w1XhMJhd9hNhIsXRXGAtqdtt4utQ\/640?wx_fmt=png&amp;wxfrom=5&amp;wx_lazy=1\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjt15WMXNepUAeNfvVicZHdXJcZWT4w1XhMJhd9hNhIsXRXGAtqdtt4utQ\/0?wx_fmt=png\" data-type=\"png\" data-ratio=\"0.6762589928057554\" data-w=\"\" \/><\/p>\n<p>\u73b0\u5728\u6211\u4eec\u8981\u5728\u56fe\u4e2d\u627e\u5230\u4e00\u6761\u76f4\u7ebf\u80fd\u6700\u5927\u7a0b\u5ea6\u5c06\u4e0d\u540c\u7ec4\u7684\u70b9\u5206\u5f00\u3002\u4e24\u7ec4\u6570\u636e\u4e2d\u8ddd\u79bb\u8fd9\u6761\u7ebf\u6700\u8fd1\u7684\u70b9\u5230\u8fd9\u6761\u7ebf\u7684\u8ddd\u79bb\u90fd\u5e94\u8be5\u662f\u6700\u8fdc\u7684\u3002<\/p>\n<p><img decoding=\"async\" class=\"\" title=\"\" src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjtN6A6hUZnQNfLk8RSunz5wwZsNeRkO6bNIIfmjojhDM1bv0DTicQHRibg\/640?wx_fmt=png&amp;wxfrom=5&amp;wx_lazy=1\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjtN6A6hUZnQNfLk8RSunz5wwZsNeRkO6bNIIfmjojhDM1bv0DTicQHRibg\/0?wx_fmt=png\" data-type=\"png\" data-ratio=\"0.6798561151079137\" data-w=\"\" \/><\/p>\n<p>\u5728\u4e0a\u56fe\u4e2d\uff0c\u9ed1\u8272\u7684\u7ebf\u5c31\u662f\u6700\u4f73\u5206\u5272\u7ebf\u3002\u56e0\u4e3a\u8fd9\u6761\u7ebf\u5230\u4e24\u7ec4\u4e2d\u8ddd\u5b83\u6700\u8fd1\u7684\u70b9\uff0c\u70b9A\u548cB\u7684\u8ddd\u79bb\u90fd\u662f\u6700\u8fdc\u7684\u3002\u4efb\u4f55\u5176\u4ed6\u7ebf\u5fc5\u7136\u4f1a\u4f7f\u5f97\u5230\u5176\u4e2d\u4e00\u4e2a\u70b9\u7684\u8ddd\u79bb\u6bd4\u8fd9\u4e2a\u8ddd\u79bb\u8fd1\u3002\u8fd9\u6837\u6839\u636e\u6570\u636e\u70b9\u5206\u5e03\u5728\u8fd9\u6761\u7ebf\u7684\u54ea\u4e00\u8fb9\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u5c06\u6570\u636e\u5f52\u7c7b\u3002<\/p>\n<p><strong>\u66f4\u591a\u9605\u8bfb<\/strong>\uff1aSimplified Version of Support Vector Machine<\/p>\n<h4><\/h4>\n<h4>\u6211\u4eec\u53ef\u4ee5\u628a\u8fd9\u4e2a\u7b97\u6cd5\u60f3\u6210n\u7ef4\u7a7a\u95f4\u91cc\u7684JezzBall\u6e38\u620f\uff0c\u4e0d\u8fc7\u6709\u4e00\u4e9b\u53d8\u52a8\uff1a<\/h4>\n<ul class=\" list-paddingleft-2\">\n<li>\u4f60\u53ef\u4ee5\u4ee5\u4efb\u4f55\u89d2\u5ea6\u753b\u5206\u5272\u7ebf\/\u5206\u5272\u9762\uff08\u7ecf\u5178\u6e38\u620f\u4e2d\u53ea\u6709\u5782\u76f4\u548c\u6c34\u5e73\u65b9\u5411\uff09\u3002<\/li>\n<li>\u73b0\u5728\u8fd9\u4e2a\u6e38\u620f\u7684\u76ee\u7684\u662f\u628a\u4e0d\u540c\u989c\u8272\u7684\u5c0f\u7403\u5206\u5230\u4e0d\u540c\u7a7a\u95f4\u91cc\u3002<\/li>\n<li>\u5c0f\u7403\u662f\u4e0d\u52a8\u7684\u3002<\/li>\n<\/ul>\n<p><em><strong>Python \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-Python\">#Import Library<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"kwd\">from<\/span><span class=\"pln\"> sklearn <\/span><span class=\"kwd\">import<\/span><span class=\"pln\"> svm<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset<\/code><\/li>\n<li><code class=\"language-Python\"># Create SVM classification object <\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">model <\/span><span class=\"pun\">=<\/span><span class=\"pln\"> svm<\/span><span class=\"pun\">.<\/span><span class=\"pln\">svc<\/span><span class=\"pun\">()<\/span> <span class=\"com\"># there is various option associated with it, this is simple for classification. You can refer link, for mo# re detail.<\/span><\/code><\/li>\n<li><code class=\"language-Python\"># Train the model using the training sets and check score<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">(<\/span><span class=\"pln\">X<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> y<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">score<\/span><span class=\"pun\">(<\/span><span class=\"pln\">X<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> y<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Predict Output<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<p><em><strong>R \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-R\"><span class=\"pln\">library<\/span><span class=\"pun\">(<\/span><span class=\"pln\">e1071<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">x <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> cbind<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_train<\/span><span class=\"pun\">,<\/span><span class=\"pln\">y_train<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"># Fitting model<\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">fit <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\">svm<\/span><span class=\"pun\">(<\/span><span class=\"pln\">y_train <\/span><span class=\"pun\">~<\/span> <span class=\"pun\">.,<\/span><span class=\"pln\"> data <\/span><span class=\"pun\">=<\/span><span class=\"pln\"> x<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">summary<\/span><span class=\"pun\">(<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\">#Predict Output <\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">,<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<h3><\/h3>\n<h3>5. \u6734\u7d20\u8d1d\u53f6\u65af<\/h3>\n<p>\u8fd9\u4e2a\u7b97\u6cd5\u662f\u5efa\u7acb\u5728<a target=\"_blank\">\u8d1d\u53f6\u65af\u7406\u8bba<\/a>\u4e0a\u7684\u5206\u7c7b\u65b9\u6cd5\u3002\u5b83\u7684\u5047\u8bbe\u6761\u4ef6\u662f\u81ea\u53d8\u91cf\u4e4b\u95f4\u76f8\u4e92\u72ec\u7acb\u3002\u7b80\u8a00\u4e4b\uff0c\u6734\u7d20\u8d1d\u53f6\u65af\u5047\u5b9a\u67d0\u4e00\u7279\u5f81\u7684\u51fa\u73b0\u4e0e\u5176\u5b83\u7279\u5f81\u65e0\u5173\u3002\u6bd4\u5982\u8bf4\uff0c\u5982\u679c\u4e00\u4e2a\u6c34\u679c\u5b83\u662f\u7ea2\u8272\u7684\uff0c\u5706\u72b6\u7684\uff0c\u76f4\u5f84\u5927\u69827cm\u5de6\u53f3\uff0c\u6211\u4eec\u53ef\u80fd\u731c\u6d4b\u5b83\u4e3a\u82f9\u679c\u3002\u5373\u4f7f\u8fd9\u4e9b\u7279\u5f81\u4e4b\u95f4\u5b58\u5728\u4e00\u5b9a\u5173\u7cfb\uff0c\u5728\u6734\u7d20\u8d1d\u53f6\u65af\u7b97\u6cd5\u4e2d\u6211\u4eec\u90fd\u8ba4\u4e3a\u7ea2\u8272\uff0c\u5706\u72b6\u548c\u76f4\u5f84\u5728\u5224\u65ad\u4e00\u4e2a\u6c34\u679c\u662f\u82f9\u679c\u7684\u53ef\u80fd\u6027\u4e0a\u662f\u76f8\u4e92\u72ec\u7acb\u7684\u3002<\/p>\n<p>\u6734\u7d20\u8d1d\u53f6\u65af\u7684\u6a21\u578b\u6613\u4e8e\u5efa\u9020\uff0c\u5e76\u4e14\u5728\u5206\u6790\u5927\u91cf\u6570\u636e\u95ee\u9898\u65f6\u6548\u7387\u5f88\u9ad8\u3002\u867d\u7136\u6a21\u578b\u7b80\u5355\uff0c\u4f46\u5f88\u591a\u60c5\u51b5\u4e0b\u5de5\u4f5c\u5f97\u6bd4\u975e\u5e38\u590d\u6742\u7684\u5206\u7c7b\u65b9\u6cd5\u8fd8\u8981\u597d\u3002<\/p>\n<p>\u8d1d\u53f6\u65af\u7406\u8bba\u544a\u8bc9\u6211\u4eec\u5982\u4f55\u4ece\u5148\u9a8c\u6982\u7387P(c),P(x)\u548c\u6761\u4ef6\u6982\u7387P(x|c)\u4e2d\u8ba1\u7b97\u540e\u9a8c\u6982\u7387P(c|x)\u3002\u7b97\u6cd5\u5982\u4e0b\uff1a<br \/>\n<img decoding=\"async\" class=\"\" title=\"\" src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjtcfauZIMytiaO0meN5L9OibfSXBCJMiaU8cygS52N9m20UQ8UgBlcgOicQg\/640?wx_fmt=png&amp;wxfrom=5&amp;wx_lazy=1\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjtcfauZIMytiaO0meN5L9OibfSXBCJMiaU8cygS52N9m20UQ8UgBlcgOicQg\/0?wx_fmt=png\" data-type=\"png\" data-ratio=\"0.5726681127982647\" data-w=\"461\" \/><\/p>\n<ul class=\" list-paddingleft-2\">\n<li>P(c|x)\u662f\u5df2\u77e5\u7279\u5f81x\u800c\u5206\u7c7b\u4e3ac\u7684\u540e\u9a8c\u6982\u7387\u3002<\/li>\n<li>P(c)\u662f\u79cd\u7c7bc\u7684\u5148\u9a8c\u6982\u7387\u3002<\/li>\n<li>P(x|c)\u662f\u79cd\u7c7bc\u5177\u6709\u7279\u5f81x\u7684\u53ef\u80fd\u6027\u3002<\/li>\n<li>P(x)\u662f\u7279\u5f81x\u7684\u5148\u9a8c\u6982\u7387\u3002<\/li>\n<\/ul>\n<p><strong>\u4f8b\u5b50\uff1a<\/strong>\u00a0\u4ee5\u4e0b\u8fd9\u7ec4\u8bad\u7ec3\u96c6\u5305\u62ec\u4e86\u5929\u6c14\u53d8\u91cf\u548c\u76ee\u6807\u53d8\u91cf\u201c\u662f\u5426\u51fa\u53bb\u73a9\u201d\u3002\u6211\u4eec\u73b0\u5728\u9700\u8981\u6839\u636e\u5929\u6c14\u60c5\u51b5\u5c06\u4eba\u4eec\u5206\u4e3a\u4e24\u7ec4\uff1a\u73a9\u6216\u4e0d\u73a9\u3002\u6574\u4e2a\u8fc7\u7a0b\u6309\u7167\u5982\u4e0b\u6b65\u9aa4\u8fdb\u884c\uff1a<\/p>\n<p>\u6b65\u9aa41\uff1a\u6839\u636e\u5df2\u77e5\u6570\u636e\u505a\u9891\u7387\u8868<\/p>\n<p>\u6b65\u9aa42\uff1a\u8ba1\u7b97\u5404\u4e2a\u60c5\u51b5\u7684\u6982\u7387\u5236\u4f5c\u6982\u7387\u8868\u3002\u6bd4\u5982\u9634\u5929\uff08Overcast\uff09\u7684\u6982\u7387\u4e3a0.29\uff0c\u6b64\u65f6\u73a9\u7684\u6982\u7387\u4e3a0.64.<\/p>\n<p><img decoding=\"async\" class=\"\" title=\"\" src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjtAHbaDuwRdQwo4WXDQOXuzPwc5asfdMceErZDOiaCByWhTWczBNcACkw\/640?wx_fmt=png&amp;wxfrom=5&amp;wx_lazy=1\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjtAHbaDuwRdQwo4WXDQOXuzPwc5asfdMceErZDOiaCByWhTWczBNcACkw\/0?wx_fmt=png\" data-ratio=\"0.36510791366906475\" data-w=\"\" data-type=\"png\" \/><\/p>\n<p>\u6b65\u9aa43\uff1a\u7528\u6734\u7d20\u8d1d\u53f6\u65af\u8ba1\u7b97\u6bcf\u79cd\u5929\u6c14\u60c5\u51b5\u4e0b\u73a9\u548c\u4e0d\u73a9\u7684\u540e\u9a8c\u6982\u7387\u3002\u6982\u7387\u5927\u7684\u7ed3\u679c\u4e3a\u9884\u6d4b\u503c\u3002<\/p>\n<p><strong>\u63d0\u95ee:<\/strong>\u00a0\u5929\u6c14\u6674\u6717\u7684\u60c5\u51b5\u4e0b(sunny)\uff0c\u4eba\u4eec\u4f1a\u73a9\u3002\u8fd9\u53e5\u9648\u8ff0\u662f\u5426\u6b63\u786e\uff1f<\/p>\n<p>\u6211\u4eec\u53ef\u4ee5\u7528\u4e0a\u8ff0\u65b9\u6cd5\u56de\u7b54\u8fd9\u4e2a\u95ee\u9898\u3002P(Yes | Sunny)=P(Sunny | Yes) * P(Yes) \/ P(Sunny)\u3002<br \/>\n\u8fd9\u91cc\uff0cP(Sunny |Yes) = 3\/9 = 0.33, P(Sunny) = 5\/14 = 0.36, P(Yes)= 9\/14 = 0.64\u3002<\/p>\n<p>\u90a3\u4e48\uff0cP (Yes | Sunny) = 0.33 * 0.64 \/ 0.36 = 0.60&gt;0.5,\u8bf4\u660e\u8fd9\u4e2a\u6982\u7387\u503c\u66f4\u5927\u3002<\/p>\n<p>\u5f53\u6709\u591a\u79cd\u7c7b\u522b\u548c\u591a\u79cd\u7279\u5f81\u65f6\uff0c\u9884\u6d4b\u7684\u65b9\u6cd5\u76f8\u4f3c\u3002\u6734\u7d20\u8d1d\u53f6\u65af\u901a\u5e38\u7528\u4e8e<strong>\u6587\u672c\u5206\u7c7b\u548c\u591a\u7c7b\u522b\u5206\u7c7b<\/strong>\u95ee\u9898\u3002<\/p>\n<p><em><strong>Python \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-Python\">#Import Library<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"kwd\">from<\/span><span class=\"pln\"> sklearn<\/span><span class=\"pun\">.<\/span><span class=\"pln\">naive_bayes <\/span><span class=\"kwd\">import<\/span> <span class=\"typ\">GaussianNB<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset<\/code><\/li>\n<li><code class=\"language-Python\"># Create SVM classification object model = GaussianNB() # there is other distribution for multinomial classes like Bernoulli Naive Bayes, Refer link<\/code><\/li>\n<li><code class=\"language-Python\"># Train the model using the training sets and check score<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">(<\/span><span class=\"pln\">X<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> y<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Predict Output<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<p><em><strong>R \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-R\"><span class=\"pln\">library<\/span><span class=\"pun\">(<\/span><span class=\"pln\">e1071<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">x <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> cbind<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_train<\/span><span class=\"pun\">,<\/span><span class=\"pln\">y_train<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"># Fitting model<\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">fit <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\">naiveBayes<\/span><span class=\"pun\">(<\/span><span class=\"pln\">y_train <\/span><span class=\"pun\">~<\/span> <span class=\"pun\">.,<\/span><span class=\"pln\"> data <\/span><span class=\"pun\">=<\/span><span class=\"pln\"> x<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">summary<\/span><span class=\"pun\">(<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\">#Predict Output <\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">,<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<h3><\/h3>\n<h3>6.KNN\uff08K-\u90bb\u8fd1\u7b97\u6cd5\uff09<\/h3>\n<p>\u8fd9\u4e2a\u7b97\u6cd5\u65e2\u53ef\u4ee5\u89e3\u51b3\u5206\u7c7b\u95ee\u9898\uff0c\u4e5f\u53ef\u4ee5\u7528\u4e8e\u56de\u5f52\u95ee\u9898\uff0c\u4f46\u5de5\u4e1a\u4e0a\u7528\u4e8e\u5206\u7c7b\u7684\u60c5\u51b5\u66f4\u591a\u3002 KNN\u5148\u8bb0\u5f55\u6240\u6709\u5df2\u77e5\u6570\u636e\uff0c\u518d\u5229\u7528\u4e00\u4e2a\u8ddd\u79bb\u51fd\u6570\uff0c\u627e\u51fa\u5df2\u77e5\u6570\u636e\u4e2d\u8ddd\u79bb\u672a\u77e5\u4e8b\u4ef6\u6700\u8fd1\u7684K\u7ec4\u6570\u636e\uff0c\u6700\u540e\u6309\u7167\u8fd9K\u7ec4\u6570\u636e\u91cc\u6700\u5e38\u89c1\u7684\u7c7b\u522b\u9884\u6d4b\u8be5\u4e8b\u4ef6\u3002<\/p>\n<p>\u8ddd\u79bb\u51fd\u6570\u53ef\u4ee5\u662f\u6b27\u5f0f\u8ddd\u79bb\uff0c\u66fc\u54c8\u987f\u8ddd\u79bb\uff0c\u95f5\u6c0f\u8ddd\u79bb (Minkowski Distance), \u548c\u6c49\u660e\u8ddd\u79bb\uff08Hamming Distance\uff09\u3002\u524d\u4e09\u79cd\u7528\u4e8e\u8fde\u7eed\u53d8\u91cf\uff0c\u6c49\u660e\u8ddd\u79bb\u7528\u4e8e\u5206\u7c7b\u53d8\u91cf\u3002\u5982\u679cK=1\uff0c\u90a3\u95ee\u9898\u5c31\u7b80\u5316\u4e3a\u6839\u636e\u6700\u8fd1\u7684\u6570\u636e\u5206\u7c7b\u3002K\u503c\u7684\u9009\u53d6\u65f6\u5e38\u662fKNN\u5efa\u6a21\u91cc\u7684\u5173\u952e\u3002<\/p>\n<p><img decoding=\"async\" class=\"\" title=\"\" src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjt84Kt8L02obFEq3flhDdA5PoPopnhc5p1ibKEWDnvXV2xxWAZewJn3qQ\/640?wx_fmt=png&amp;wxfrom=5&amp;wx_lazy=1\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjt84Kt8L02obFEq3flhDdA5PoPopnhc5p1ibKEWDnvXV2xxWAZewJn3qQ\/0?wx_fmt=png\" data-type=\"png\" data-ratio=\"0.4370503597122302\" data-w=\"\" \/><\/p>\n<p>KNN\u5728\u751f\u6d3b\u4e2d\u7684\u8fd0\u7528\u5f88\u591a\u3002\u6bd4\u5982\uff0c\u5982\u679c\u4f60\u60f3\u4e86\u89e3\u4e00\u4e2a\u4e0d\u8ba4\u8bc6\u7684\u4eba\uff0c\u4f60\u53ef\u80fd\u5c31\u4f1a\u4ece\u8fd9\u4e2a\u4eba\u7684\u597d\u670b\u53cb\u548c\u5708\u5b50\u4e2d\u4e86\u89e3\u4ed6\u7684\u4fe1\u606f\u3002<\/p>\n<p><strong>\u5728\u7528KNN\u524d\u4f60\u9700\u8981\u8003\u8651\u5230\uff1a<\/strong><\/p>\n<ul class=\" list-paddingleft-2\">\n<li>KNN\u7684\u8ba1\u7b97\u6210\u672c\u5f88\u9ad8<\/li>\n<li>\u6240\u6709\u7279\u5f81\u5e94\u8be5\u6807\u51c6\u5316\u6570\u91cf\u7ea7\uff0c\u5426\u5219\u6570\u91cf\u7ea7\u5927\u7684\u7279\u5f81\u5728\u8ba1\u7b97\u8ddd\u79bb\u4e0a\u4f1a\u6709\u504f\u79fb\u3002<\/li>\n<li>\u5728\u8fdb\u884cKNN\u524d\u9884\u5904\u7406\u6570\u636e\uff0c\u4f8b\u5982\u53bb\u9664\u5f02\u5e38\u503c\uff0c\u566a\u97f3\u7b49\u3002<\/li>\n<\/ul>\n<p><em><strong>Python \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-Python\">#Import Library<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"kwd\">from<\/span><span class=\"pln\"> sklearn<\/span><span class=\"pun\">.<\/span><span class=\"pln\">neighbors <\/span><span class=\"kwd\">import<\/span> <span class=\"typ\">KNeighborsClassifier<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset<\/code><\/li>\n<li><code class=\"language-Python\"># Create KNeighbors classifier object model <\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"typ\">KNeighborsClassifier<\/span><span class=\"pun\">(<\/span><span class=\"pln\">n_neighbors<\/span><span class=\"pun\">=<\/span><span class=\"lit\">6<\/span><span class=\"pun\">)<\/span> <span class=\"com\"># default value for n_neighbors is 5<\/span><\/code><\/li>\n<li><code class=\"language-Python\"># Train the model using the training sets and check score<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">(<\/span><span class=\"pln\">X<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> y<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Predict Output<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<p><em><strong>R \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-R\"><span class=\"pln\">library<\/span><span class=\"pun\">(<\/span><span class=\"pln\">knn<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">x <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> cbind<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_train<\/span><span class=\"pun\">,<\/span><span class=\"pln\">y_train<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"># Fitting model<\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">fit <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\">knn<\/span><span class=\"pun\">(<\/span><span class=\"pln\">y_train <\/span><span class=\"pun\">~<\/span> <span class=\"pun\">.,<\/span><span class=\"pln\"> data <\/span><span class=\"pun\">=<\/span><span class=\"pln\"> x<\/span><span class=\"pun\">,<\/span><span class=\"pln\">k<\/span><span class=\"pun\">=<\/span><span class=\"lit\">5<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">summary<\/span><span class=\"pun\">(<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\">#Predict Output <\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">,<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<h3><\/h3>\n<h3>7. K\u5747\u503c\u7b97\u6cd5\uff08K-Means\uff09<\/h3>\n<p>\u8fd9\u662f\u4e00\u79cd\u89e3\u51b3\u805a\u7c7b\u95ee\u9898\u7684\u975e\u76d1\u7763\u5f0f\u5b66\u4e60\u7b97\u6cd5\u3002\u8fd9\u4e2a\u65b9\u6cd5\u7b80\u5355\u5730\u5229\u7528\u4e86\u4e00\u5b9a\u6570\u91cf\u7684\u96c6\u7fa4\uff08\u5047\u8bbeK\u4e2a\u96c6\u7fa4\uff09\u5bf9\u7ed9\u5b9a\u6570\u636e\u8fdb\u884c\u5206\u7c7b\u3002\u540c\u4e00\u96c6\u7fa4\u5185\u7684\u6570\u636e\u70b9\u662f\u540c\u7c7b\u7684\uff0c\u4e0d\u540c\u96c6\u7fa4\u7684\u6570\u636e\u70b9\u4e0d\u540c\u7c7b\u3002<\/p>\n<p>\u8fd8\u8bb0\u5f97\u4f60\u662f\u600e\u6837\u4ece\u58a8\u6c34\u6e0d\u4e2d\u8fa8\u8ba4\u5f62\u72b6\u7684\u4e48\uff1fK\u5747\u503c\u7b97\u6cd5\u7684\u8fc7\u7a0b\u7c7b\u4f3c\uff0c\u4f60\u4e5f\u8981\u901a\u8fc7\u89c2\u5bdf\u96c6\u7fa4\u5f62\u72b6\u548c\u5206\u5e03\u6765\u5224\u65ad\u96c6\u7fa4\u6570\u91cf\uff01<\/p>\n<p><img decoding=\"async\" class=\"\" title=\"\" src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjt6Ay5ZhkpsEAz5dRwRXqby9HOk2vibr9faKjI8d3Aiah735oWibWwKcdFA\/640?wx_fmt=jpeg&amp;wxfrom=5&amp;wx_lazy=1\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjt6Ay5ZhkpsEAz5dRwRXqby9HOk2vibr9faKjI8d3Aiah735oWibWwKcdFA\/0?wx_fmt=jpeg\" data-type=\"jpeg\" data-ratio=\"1.056338028169014\" data-w=\"284\" \/><\/p>\n<p><strong>K\u5747\u503c\u7b97\u6cd5\u5982\u4f55\u5212\u5206\u96c6\u7fa4\uff1a<\/strong><\/p>\n<ol class=\" list-paddingleft-2\">\n<li>\u4ece\u6bcf\u4e2a\u96c6\u7fa4\u4e2d\u9009\u53d6K\u4e2a\u6570\u636e\u70b9\u4f5c\u4e3a\u8d28\u5fc3\uff08centroids\uff09\u3002<\/li>\n<li>\u5c06\u6bcf\u4e00\u4e2a\u6570\u636e\u70b9\u4e0e\u8ddd\u79bb\u81ea\u5df1\u6700\u8fd1\u7684\u8d28\u5fc3\u5212\u5206\u5728\u540c\u4e00\u96c6\u7fa4\uff0c\u5373\u751f\u6210K\u4e2a\u65b0\u96c6\u7fa4\u3002<\/li>\n<li>\u627e\u51fa\u65b0\u96c6\u7fa4\u7684\u8d28\u5fc3\uff0c\u8fd9\u6837\u5c31\u6709\u4e86\u65b0\u7684\u8d28\u5fc3\u3002<\/li>\n<li>\u91cd\u590d2\u548c3\uff0c\u76f4\u5230\u7ed3\u679c\u6536\u655b\uff0c\u5373\u4e0d\u518d\u6709\u65b0\u7684\u8d28\u5fc3\u51fa\u73b0\u3002<\/li>\n<\/ol>\n<p><strong>\u600e\u6837\u786e\u5b9aK\u7684\u503c\uff1a<\/strong><\/p>\n<p>\u5982\u679c\u6211\u4eec\u5728\u6bcf\u4e2a\u96c6\u7fa4\u4e2d\u8ba1\u7b97\u96c6\u7fa4\u4e2d\u6240\u6709\u70b9\u5230\u8d28\u5fc3\u7684\u8ddd\u79bb\u5e73\u65b9\u548c\uff0c\u518d\u5c06\u4e0d\u540c\u96c6\u7fa4\u7684\u8ddd\u79bb\u5e73\u65b9\u548c\u76f8\u52a0\uff0c\u6211\u4eec\u5c31\u5f97\u5230\u4e86\u8fd9\u4e2a\u96c6\u7fa4\u65b9\u6848\u7684\u603b\u5e73\u65b9\u548c\u3002<\/p>\n<p>\u6211\u4eec\u77e5\u9053\uff0c\u968f\u7740\u96c6\u7fa4\u6570\u91cf\u7684\u589e\u52a0\uff0c\u603b\u5e73\u65b9\u548c\u4f1a\u51cf\u5c11\u3002\u4f46\u662f\u5982\u679c\u7528\u603b\u5e73\u65b9\u548c\u5bf9K\u4f5c\u56fe\uff0c\u4f60\u4f1a\u53d1\u73b0\u5728\u67d0\u4e2aK\u503c\u4e4b\u524d\u603b\u5e73\u65b9\u548c\u6025\u901f\u51cf\u5c11\uff0c\u4f46\u5728\u8fd9\u4e2aK\u503c\u4e4b\u540e\u51cf\u5c11\u7684\u5e45\u5ea6\u5927\u5927\u964d\u4f4e\uff0c\u8fd9\u4e2a\u503c\u5c31\u662f\u6700\u4f73\u7684\u96c6\u7fa4\u6570\u3002<\/p>\n<p><img decoding=\"async\" class=\"\" title=\"\" src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjtfRcDccWIIjMZSspM1O3ENNNBJ5sJ2SBCiaWMb1TBNKuqicaJAyWt3TUQ\/640?wx_fmt=png&amp;wxfrom=5&amp;wx_lazy=1\" data-src=\"http:\/\/mmbiz.qpic.cn\/mmbiz\/wc7YNPm3YxWzUMCDSvdMsMeQlwohibWjtfRcDccWIIjMZSspM1O3ENNNBJ5sJ2SBCiaWMb1TBNKuqicaJAyWt3TUQ\/0?wx_fmt=png\" data-type=\"png\" data-ratio=\"0.5053956834532374\" data-w=\"\" \/><\/p>\n<p><em><strong>Python \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-Python\">#Import Library<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"kwd\">from<\/span><span class=\"pln\"> sklearn<\/span><span class=\"pun\">.<\/span><span class=\"pln\">cluster <\/span><span class=\"kwd\">import<\/span> <span class=\"typ\">KMeans<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Assumed you have, X (attributes) for training data set and x_test(attributes) of test_dataset<\/code><\/li>\n<li><code class=\"language-Python\"># Create KNeighbors classifier object model <\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">k_means <\/span><span class=\"pun\">=<\/span> <span class=\"typ\">KMeans<\/span><span class=\"pun\">(<\/span><span class=\"pln\">n_clusters<\/span><span class=\"pun\">=<\/span><span class=\"lit\">3<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> random_state<\/span><span class=\"pun\">=<\/span><span class=\"lit\">0<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-Python\"># Train the model using the training sets and check score<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">(<\/span><span class=\"pln\">X<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Predict Output<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<p><em><strong>R \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-R\"><span class=\"pln\">library<\/span><span class=\"pun\">(<\/span><span class=\"pln\">cluster<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">fit <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> kmeans<\/span><span class=\"pun\">(<\/span><span class=\"pln\">X<\/span><span class=\"pun\">,<\/span> <span class=\"lit\">3<\/span><span class=\"pun\">)<\/span> <span class=\"com\"># 5 cluster solution<\/span><\/code><\/li>\n<\/ol>\n<h3><\/h3>\n<h3>8.\u968f\u673a\u68ee\u6797<\/h3>\n<p>\u968f\u673a\u68ee\u6797\u662f\u5bf9\u51b3\u7b56\u6811\u96c6\u5408\u7684\u7279\u6709\u540d\u79f0\u3002\u968f\u673a\u68ee\u6797\u91cc\u6211\u4eec\u6709\u591a\u4e2a\u51b3\u7b56\u6811\uff08\u6240\u4ee5\u53eb\u201c\u68ee\u6797\u201d\uff09\u3002\u4e3a\u4e86\u7ed9\u4e00\u4e2a\u65b0\u7684\u89c2\u5bdf\u503c\u5206\u7c7b\uff0c\u6839\u636e\u5b83\u7684\u7279\u5f81\uff0c\u6bcf\u4e00\u4e2a\u51b3\u7b56\u6811\u90fd\u4f1a\u7ed9\u51fa\u4e00\u4e2a\u5206\u7c7b\u3002\u968f\u673a\u68ee\u6797\u7b97\u6cd5\u9009\u51fa\u6295\u7968\u6700\u591a\u7684\u5206\u7c7b\u4f5c\u4e3a\u5206\u7c7b\u7ed3\u679c\u3002<\/p>\n<p>\u600e\u6837\u751f\u6210\u51b3\u7b56\u6811\uff1a<\/p>\n<ol class=\" list-paddingleft-2\">\n<li>\u5982\u679c\u8bad\u7ec3\u96c6\u4e2d\u6709N\u79cd\u7c7b\u522b\uff0c\u5219\u6709\u91cd\u590d\u5730\u968f\u673a\u9009\u53d6N\u4e2a\u6837\u672c\u3002\u8fd9\u4e9b\u6837\u672c\u5c06\u7ec4\u6210\u57f9\u517b\u51b3\u7b56\u6811\u7684\u8bad\u7ec3\u96c6\u3002<\/li>\n<li>\u5982\u679c\u6709M\u4e2a\u7279\u5f81\u53d8\u91cf\uff0c\u90a3\u4e48\u9009\u53d6\u6570m &lt;&lt; M\uff0c\u4ece\u800c\u5728\u6bcf\u4e2a\u8282\u70b9\u4e0a\u968f\u673a\u9009\u53d6m\u4e2a\u7279\u5f81\u53d8\u91cf\u6765\u5206\u5272\u8be5\u8282\u70b9\u3002m\u5728\u6574\u4e2a\u68ee\u6797\u517b\u6210\u4e2d\u4fdd\u6301\u4e0d\u53d8\u3002<\/li>\n<li>\u6bcf\u4e2a\u51b3\u7b56\u6811\u90fd\u6700\u5927\u7a0b\u5ea6\u4e0a\u8fdb\u884c\u5206\u5272\uff0c\u6ca1\u6709\u526a\u679d\u3002<\/li>\n<\/ol>\n<p>\u6bd4\u8f83\u51b3\u7b56\u6811\u548c\u8c03\u8282\u6a21\u578b\u53c2\u6570\u53ef\u4ee5\u83b7\u53d6\u66f4\u591a\u8be5\u7b97\u6cd5\u7ec6\u8282\u3002\u6211\u5efa\u8bae\u8bfb\u8005\u9605\u8bfb\u8fd9\u4e9b\u6587\u7ae0\uff1a<\/p>\n<ol class=\" list-paddingleft-2\">\n<li>Introduction to Random forest \u2013 Simplified<\/li>\n<li>Comparing a CART model to Random Forest (Part 1)<\/li>\n<li>Comparing a Random Forest to a CART model (Part 2)<\/li>\n<li>Tuning the parameters of your Random Forest model<\/li>\n<\/ol>\n<p><em><strong>Python \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-Python\">#Import Library<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"kwd\">from<\/span><span class=\"pln\"> sklearn<\/span><span class=\"pun\">.<\/span><span class=\"pln\">ensemble <\/span><span class=\"kwd\">import<\/span> <span class=\"typ\">RandomForestClassifier<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset<\/code><\/li>\n<li><code class=\"language-Python\"># Create Random Forest object<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">model<\/span><span class=\"pun\">=<\/span> <span class=\"typ\">RandomForestClassifier<\/span><span class=\"pun\">()<\/span><\/code><\/li>\n<li><code class=\"language-Python\"># Train the model using the training sets and check score<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">(<\/span><span class=\"pln\">X<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> y<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Predict Output<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<p><em><strong>R \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-R\"><span class=\"pln\">library<\/span><span class=\"pun\">(<\/span><span class=\"pln\">randomForest<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">x <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> cbind<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_train<\/span><span class=\"pun\">,<\/span><span class=\"pln\">y_train<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"># Fitting model<\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">fit <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> randomForest<\/span><span class=\"pun\">(<\/span><span class=\"typ\">Species<\/span> <span class=\"pun\">~<\/span> <span class=\"pun\">.,<\/span><span class=\"pln\"> x<\/span><span class=\"pun\">,<\/span><span class=\"pln\">ntree<\/span><span class=\"pun\">=<\/span><span class=\"lit\">500<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">summary<\/span><span class=\"pun\">(<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\">#Predict Output <\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">,<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<h3><\/h3>\n<h3>9.\u964d\u7ef4\u7b97\u6cd5\uff08Dimensionality Reduction Algorithms\uff09<\/h3>\n<p>\u5728\u8fc7\u53bb\u76844-5\u5e74\u91cc\uff0c\u53ef\u83b7\u53d6\u7684\u6570\u636e\u51e0\u4e4e\u4ee5\u6307\u6570\u5f62\u5f0f\u589e\u957f\u3002\u516c\u53f8\/\u653f\u5e9c\u673a\u6784\/\u7814\u7a76\u7ec4\u7ec7\u4e0d\u4ec5\u6709\u4e86\u66f4\u591a\u7684\u6570\u636e\u6765\u6e90\uff0c\u4e5f\u83b7\u5f97\u4e86\u66f4\u591a\u7ef4\u5ea6\u7684\u6570\u636e\u4fe1\u606f\u3002<\/p>\n<p>\u4f8b\u5982\uff1a\u7535\u5b50\u5546\u52a1\u516c\u53f8\u6709\u4e86\u987e\u5ba2\u66f4\u591a\u7684\u7ec6\u8282\u4fe1\u606f\uff0c\u50cf\u4e2a\u4eba\u4fe1\u606f\uff0c\u7f51\u7edc\u6d4f\u89c8\u5386\u53f2\uff0c\u4e2a\u4eba\u559c\u6076\uff0c\u8d2d\u4e70\u8bb0\u5f55\uff0c\u53cd\u9988\u4fe1\u606f\u7b49\uff0c\u4ed6\u4eec\u5173\u6ce8\u4f60\u7684\u79c1\u4eba\u7279\u5f81\uff0c\u6bd4\u4f60\u5929\u5929\u53bb\u7684\u8d85\u5e02\u91cc\u7684\u5e97\u5458\u66f4\u4e86\u89e3\u4f60\u3002<\/p>\n<p>\u4f5c\u4e3a\u4e00\u540d\u6570\u636e\u79d1\u5b66\u5bb6\uff0c\u6211\u4eec\u624b\u4e0a\u7684\u6570\u636e\u6709\u975e\u5e38\u591a\u7684\u7279\u5f81\u3002\u867d\u7136\u8fd9\u542c\u8d77\u6765\u6709\u5229\u4e8e\u5efa\u7acb\u66f4\u5f3a\u5927\u7cbe\u51c6\u7684\u6a21\u578b\uff0c\u4f46\u5b83\u4eec\u6709\u65f6\u5019\u53cd\u5012\u4e5f\u662f\u5efa\u6a21\u4e2d\u7684\u4e00\u5927\u96be\u9898\u3002\u600e\u6837\u624d\u80fd\u4ece1000\u62162000\u4e2a\u53d8\u91cf\u91cc\u627e\u5230\u6700\u91cd\u8981\u7684\u53d8\u91cf\u5462\uff1f\u8fd9\u79cd\u60c5\u51b5\u4e0b\u964d\u7ef4\u7b97\u6cd5\u53ca\u5176\u4ed6\u7b97\u6cd5\uff0c\u5982\u51b3\u7b56\u6811\uff0c\u968f\u673a\u68ee\u6797\uff0cPCA\uff0c\u56e0\u5b50\u5206\u6790\uff0c\u76f8\u5173\u77e9\u9635\uff0c\u548c\u7f3a\u7701\u503c\u6bd4\u4f8b\u7b49\uff0c\u5c31\u80fd\u5e2e\u6211\u4eec\u89e3\u51b3\u96be\u9898\u3002<\/p>\n<p>\u8fdb\u4e00\u6b65\u7684\u4e86\u89e3\u53ef\u4ee5\u9605\u8bfbBeginners Guide To Learn Dimension Reduction Techniques\u3002<\/p>\n<p><em><strong>Python \u4ee3\u7801<\/strong><\/em><\/p>\n<p><em>\u66f4\u591a\u4fe1\u606f\u5728<\/em><em><a target=\"_blank\">\u8fd9\u91cc<\/a><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-Python\">#Import Library<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"kwd\">from<\/span><span class=\"pln\"> sklearn <\/span><span class=\"kwd\">import<\/span><span class=\"pln\"> decomposition<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Assumed you have training and test data set as train and test<\/code><\/li>\n<li><code class=\"language-Python\"># Create PCA obeject pca= decomposition.PCA(n_components=k) #default value of k =min(n_sample, n_features)<\/code><\/li>\n<li><code class=\"language-Python\"># For Factor analysis<\/code><\/li>\n<li><code class=\"language-Python\">#fa= decomposition.FactorAnalysis()<\/code><\/li>\n<li><code class=\"language-Python\"># Reduced the dimension of training dataset using PCA<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">train_reduced <\/span><span class=\"pun\">=<\/span><span class=\"pln\"> pca<\/span><span class=\"pun\">.<\/span><span class=\"pln\">fit_transform<\/span><span class=\"pun\">(<\/span><span class=\"pln\">train<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Reduced the dimension of test dataset<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">test_reduced <\/span><span class=\"pun\">=<\/span><span class=\"pln\"> pca<\/span><span class=\"pun\">.<\/span><span class=\"pln\">transform<\/span><span class=\"pun\">(<\/span><span class=\"pln\">test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<p><em><strong>R \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-R\"><span class=\"pln\">library<\/span><span class=\"pun\">(<\/span><span class=\"pln\">stats<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">pca <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> princomp<\/span><span class=\"pun\">(<\/span><span class=\"pln\">train<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> cor <\/span><span class=\"pun\">=<\/span><span class=\"pln\"> TRUE<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">train_reduced \u00a0<\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">pca<\/span><span class=\"pun\">,<\/span><span class=\"pln\">train<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">test_reduced \u00a0<\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">pca<\/span><span class=\"pun\">,<\/span><span class=\"pln\">test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<h3><\/h3>\n<h3>10.Gradient Boosing \u548c AdaBoost<\/h3>\n<p>GBM\u548cAdaBoost\u90fd\u662f\u5728\u6709\u5927\u91cf\u6570\u636e\u65f6\u63d0\u9ad8\u9884\u6d4b\u51c6\u786e\u5ea6\u7684boosting\u7b97\u6cd5\u3002Boosting\u662f\u4e00\u79cd\u96c6\u6210\u5b66\u4e60\u65b9\u6cd5\u3002\u5b83\u901a\u8fc7\u6709\u5e8f\u7ed3\u5408\u591a\u4e2a\u8f83\u5f31\u7684\u5206\u7c7b\u5668\/\u4f30\u6d4b\u5668\u7684\u4f30\u8ba1\u7ed3\u679c\u6765\u63d0\u9ad8\u9884\u6d4b\u51c6\u786e\u5ea6\u3002\u8fd9\u4e9bboosting\u7b97\u6cd5\u5728Kaggle\uff0cAV Hackthon, CrowdAnalytix\u7b49\u6570\u636e\u79d1\u5b66\u7ade\u8d5b\u4e2d\u6709\u51fa\u8272\u53d1\u6325\u3002<\/p>\n<p><strong>\u66f4\u591a\u9605\u8bfb\uff1a<\/strong>\u00a0Know about Gradient and AdaBoost in detail<\/p>\n<p><em><strong>Python \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-Python\">#Import Library<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"kwd\">from<\/span><span class=\"pln\"> sklearn<\/span><span class=\"pun\">.<\/span><span class=\"pln\">ensemble <\/span><span class=\"kwd\">import<\/span> <span class=\"typ\">GradientBoostingClassifier<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset<\/code><\/li>\n<li><code class=\"language-Python\"># Create Gradient Boosting Classifier object<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">model<\/span><span class=\"pun\">=<\/span> <span class=\"typ\">GradientBoostingClassifier<\/span><span class=\"pun\">(<\/span><span class=\"pln\">n_estimators<\/span><span class=\"pun\">=<\/span><span class=\"lit\">100<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> learning_rate<\/span><span class=\"pun\">=<\/span><span class=\"lit\">1.0<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> max_depth<\/span><span class=\"pun\">=<\/span><span class=\"lit\">1<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> random_state<\/span><span class=\"pun\">=<\/span><span class=\"lit\">0<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-Python\"># Train the model using the training sets and check score<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">(<\/span><span class=\"pln\">X<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> y<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-Python\">#Predict Output<\/code><\/li>\n<li><code class=\"language-Python\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> model<\/span><span class=\"pun\">.<\/span><span class=\"pln\">predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<\/ol>\n<p><em><strong>R \u4ee3\u7801<\/strong><\/em><\/p>\n<ol class=\"linenums list-paddingleft-2\">\n<li><code class=\"language-R\"><span class=\"pln\">library<\/span><span class=\"pun\">(<\/span><span class=\"pln\">caret<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">x <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> cbind<\/span><span class=\"pun\">(<\/span><span class=\"pln\">x_train<\/span><span class=\"pun\">,<\/span><span class=\"pln\">y_train<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"># Fitting model<\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">fitControl <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> trainControl<\/span><span class=\"pun\">(<\/span><span class=\"pln\"> method <\/span><span class=\"pun\">=<\/span> <span class=\"str\">\"repeatedcv\"<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> number <\/span><span class=\"pun\">=<\/span> <span class=\"lit\">4<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> repeats <\/span><span class=\"pun\">=<\/span> <span class=\"lit\">4<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">fit <\/span><span class=\"pun\">&lt;-<\/span><span class=\"pln\"> train<\/span><span class=\"pun\">(<\/span><span class=\"pln\">y <\/span><span class=\"pun\">~<\/span> <span class=\"pun\">.,<\/span><span class=\"pln\"> data <\/span><span class=\"pun\">=<\/span><span class=\"pln\"> x<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> method <\/span><span class=\"pun\">=<\/span> <span class=\"str\">\"gbm\"<\/span><span class=\"pun\">,<\/span><span class=\"pln\"> trControl <\/span><span class=\"pun\">=<\/span><span class=\"pln\"> fitControl<\/span><span class=\"pun\">,<\/span><span class=\"pln\">verbose <\/span><span class=\"pun\">=<\/span><span class=\"pln\"> FALSE<\/span><span class=\"pun\">)<\/span><\/code><\/li>\n<li><code class=\"language-R\"><span class=\"pln\">predicted<\/span><span class=\"pun\">=<\/span><span class=\"pln\"> predict<\/span><span class=\"pun\">(<\/span><span class=\"pln\">fit<\/span><span class=\"pun\">,<\/span><span class=\"pln\">x_test<\/span><span class=\"pun\">,<\/span><span class=\"pln\">type<\/span><span class=\"pun\">=<\/span> <span class=\"str\">\"prob\"<\/span><span class=\"pun\">)[,<\/span><span class=\"lit\">2<\/span><span class=\"pun\">]<\/span> <\/code><\/li>\n<\/ol>\n<p>GradientBoostingClassifier \u548c\u968f\u673a\u68ee\u6797\u662f\u4e24\u79cd\u4e0d\u540c\u7684boosting\u5206\u7c7b\u6811\u3002\u4eba\u4eec\u7ecf\u5e38\u63d0\u95ee\u8fd9\u4e24\u4e2a\u7b97\u6cd5\u6709\u4ec0\u4e48\u4e0d\u540c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>2016-04-19 \u5927\u6570\u636e\u6587\u6458 \u7f16\u8bd1\uff1a@\u9152\u9152 \u6821\u6b63\uff1a\u5bd2\u5c0f\u9633\u00a0&amp;&amp;\u00a0\u9f99\u5fc3\u5c18 \u6458\u81ea\uff1ahttp:\/ &hellip; <a href=\"http:\/\/blog.softwareclues.com\/zh\/common-machine-learning-algorithms\" class=\"more-link\">\u7ee7\u7eed\u9605\u8bfb<span 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