{"id":31038,"date":"2025-04-21T07:07:32","date_gmt":"2025-04-20T23:07:32","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/31038.html"},"modified":"2025-04-21T07:07:32","modified_gmt":"2025-04-20T23:07:32","slug":"matlab%e5%a4%9a%e7%a7%8d%e7%ae%97%e6%b3%95%e8%a7%a3%e5%86%b3%e6%9c%aa%e6%9d%a5%e6%9d%afb%e7%9a%84%e5%a4%9a%e5%88%86%e7%b1%bb%e9%97%ae%e9%a2%98","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/31038.html","title":{"rendered":"Matlab\u591a\u79cd\u7b97\u6cd5\u89e3\u51b3\u672a\u6765\u676fB\u7684\u591a\u5206\u7c7b\u95ee\u9898"},"content":{"rendered":"<h3>1. \u8bfb\u53d6\u6570\u636e<\/h3>\n<p>\u9996\u5148&#xff0c;\u6211\u4eec\u4ece Excel \u6587\u4ef6\u4e2d\u8bfb\u53d6\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6&#xff1a;<\/p>\n<h3>2. \u8bad\u7ec3\u96c6\u5212\u5206<\/h3>\n<p>\u6211\u4eec\u5c06 80% \u7684\u6570\u636e\u7528\u4e8e\u8bad\u7ec3&#xff0c;20% \u7528\u4e8e\u9a8c\u8bc1\u3002<\/p>\n<h3>3. \u8bad\u7ec3\u591a\u4e2a\u6a21\u578b<\/h3>\n<p>\u6211\u4eec\u9009\u53d6 8 \u79cd\u5e38\u89c1\u5206\u7c7b\u6a21\u578b&#xff0c;\u5e76\u5b58\u50a8\u9884\u6d4b\u7ed3\u679c\u3002<\/p>\n<p>for i &#061; 1:length(modelNames)<br \/>\n    switch modelNames{i}<br \/>\n        case &#039;Multinomial Logistic Regression&#039;<br \/>\n            B &#061; mnrfit(X_train, Y_train, &#039;model&#039;, &#039;nominal&#039;);<br \/>\n            predProb &#061; mnrval(B, X_test);<br \/>\n            [~, predictions{i}] &#061; max(predProb, [], 2);<br \/>\n            predProb_val &#061; mnrval(B, X_val);<br \/>\n            [~, val_predictions] &#061; max(predProb_val, [], 2);<\/p>\n<p>        case &#039;Decision Tree&#039;<br \/>\n            model &#061; fitctree(X_train, Y_train);<br \/>\n            predictions{i} &#061; predict(model, X_test);<br \/>\n            val_predictions &#061; predict(model, X_val);<\/p>\n<p>        case &#039;Random Forest&#039;<br \/>\n            model &#061; fitcensemble(X_train, Y_train, &#039;Method&#039;, &#039;Bag&#039;);<br \/>\n            predictions{i} &#061; predict(model, X_test);<br \/>\n            val_predictions &#061; predict(model, X_val);<\/p>\n<p>        case &#039;SVM&#039;<br \/>\n            model &#061; fitcecoc(X_train, Y_train);<br \/>\n            predictions{i} &#061; predict(model, X_test);<br \/>\n            val_predictions &#061; predict(model, X_val);<\/p>\n<p>        case &#039;KNN&#039;<br \/>\n            model &#061; fitcknn(X_train, Y_train);<br \/>\n            predictions{i} &#061; predict(model, X_test);<br \/>\n            val_predictions &#061; predict(model, X_val);<\/p>\n<p>        case &#039;BP Neural Network&#039;<br \/>\n            model &#061; feedforwardnet(15);<br \/>\n            model &#061; train(model, X_train&#039;, full(ind2vec(Y_train&#039;)));<br \/>\n            nnOutput &#061; model(X_test&#039;)&#039;;<br \/>\n            [~, predictions{i}] &#061; max(nnOutput, [], 2);<br \/>\n            nnOutput_val &#061; model(X_val&#039;)&#039;;<br \/>\n            [~, val_predictions] &#061; max(nnOutput_val, [], 2);<\/p>\n<p>        case &#039;GBM&#039;<br \/>\n            model &#061; fitcensemble(X_train, Y_train, &#039;Method&#039;, &#039;AdaBoostM2&#039;);<br \/>\n            predictions{i} &#061; predict(model, X_test);<br \/>\n            val_predictions &#061; predict(model, X_val);<\/p>\n<p>        case &#039;AdaBoost&#039;<br \/>\n            model &#061; fitcensemble(X_train, Y_train, &#039;Method&#039;, &#039;AdaBoostM2&#039;);<br \/>\n            predictions{i} &#061; predict(model, X_test);<br \/>\n            val_predictions &#061; predict(model, X_val);<br \/>\n    end<\/p>\n<h3>4. \u7ed3\u679c\u8f93\u51fa<\/h3>\n<h4>&#xff08;1&#xff09;\u5b58\u50a8\u9884\u6d4b\u7ed3\u679c<\/h4>\n<p>\u5c06\u9884\u6d4b\u7ed3\u679c\u4fdd\u5b58\u4e3a Excel \u6587\u4ef6\u3002<\/p>\n<h4>&#xff08;2&#xff09;\u6a21\u578b\u8bc4\u4f30<\/h4>\n<p>\u4f7f\u7528 \u6df7\u6dc6\u77e9\u9635 \u548c \u7cbe\u5ea6\u8ba1\u7b97 \u8bc4\u4f30\u6a21\u578b\u3002<\/p>\n<h3>5. \u7ed3\u679c\u5206\u6790<\/h3>\n<ul>\n<li>\u903b\u8f91\u56de\u5f52&#xff1a;\u9002\u7528\u4e8e\u7ebf\u6027\u53ef\u5206\u6570\u636e&#xff0c;\u6613\u89e3\u91ca\u4f46\u7cbe\u5ea6\u6709\u9650\u3002<\/li>\n<li>\u51b3\u7b56\u6811&#xff1a;\u6613\u4e8e\u7406\u89e3\u4f46\u5bb9\u6613\u8fc7\u62df\u5408\u3002<\/li>\n<li>\u968f\u673a\u68ee\u6797&#xff1a;\u66f4\u7a33\u5b9a&#xff0c;\u9002\u7528\u4e8e\u9ad8\u7ef4\u6570\u636e\u3002<\/li>\n<li>SVM&#xff1a;\u9002\u7528\u4e8e\u5c0f\u6837\u672c\u6570\u636e&#xff0c;\u4f46\u8ba1\u7b97\u6210\u672c\u8f83\u9ad8\u3002<\/li>\n<li>KNN&#xff1a;\u7b80\u5355\u4f46\u8ba1\u7b97\u91cf\u5927&#xff0c;\u9002\u7528\u4e8e\u5c0f\u89c4\u6a21\u6570\u636e\u3002<\/li>\n<li>BP \u795e\u7ecf\u7f51\u7edc&#xff1a;\u53ef\u5904\u7406\u590d\u6742\u5173\u7cfb&#xff0c;\u4f46\u8bad\u7ec3\u65f6\u95f4\u957f\u3002<\/li>\n<li>GBM \u548c AdaBoost&#xff1a;\u9002\u7528\u4e8e\u975e\u7ebf\u6027\u5173\u7cfb&#xff0c;\u4f46\u8d85\u53c2\u6570\u8c03\u4f18\u91cd\u8981\u3002<\/li>\n<\/ul>\n<h3>\u603b\u7ed3<\/h3>\n<p>\u672c\u6587\u4f7f\u7528 MATLAB \u5b9e\u73b0\u4e86 8 \u79cd\u5206\u7c7b\u6a21\u578b&#xff0c;\u5e76\u8fdb\u884c\u4e86\u8bad\u7ec3\u3001\u9884\u6d4b\u548c\u8bc4\u4f30\u3002\u901a\u8fc7\u6df7\u6dc6\u77e9\u9635\u548c\u7cbe\u5ea6\u53ef\u89c6\u5316&#xff0c;\u5e2e\u52a9\u9009\u62e9\u6700\u4f18\u6a21\u578b\u3002<\/p>\n<p>\u5b8c\u6574\u4ee3\u7801&#xff1a;<\/p>\n<p>% \u5bfc\u5165\u8bad\u7ec3\u96c6<br \/>\ntrainData &#061; readtable(&#039;train.xlsx&#039;);<br \/>\nX &#061; table2array(trainData(:, 1:end-1)); % \u7279\u5f81<br \/>\nY &#061; trainData{:, end}; % \u7c7b\u522b&#xff08;\u591a\u5206\u7c7b&#xff09;<\/p>\n<p>% \u5bfc\u5165\u6d4b\u8bd5\u96c6 (\u6ca1\u6709\u6807\u7b7e\u7684\u6d4b\u8bd5\u96c6)<br \/>\ntestData &#061; readtable(&#039;test.xlsx&#039;);<br \/>\nX_test &#061; table2array(testData(:, 1:end)); % \u6d4b\u8bd5\u96c6\u7279\u5f81<\/p>\n<p>% \u5206\u5272\u6570\u636e\u96c6&#xff1a;80% \u7528\u4e8e\u8bad\u7ec3&#xff0c;20% \u7528\u4e8e\u9a8c\u8bc1<br \/>\ncv &#061; cvpartition(size(X, 1), &#039;HoldOut&#039;, 0.2);<br \/>\nX_train &#061; X(training(cv), :); % 80%\u7684\u8bad\u7ec3\u6570\u636e<br \/>\nY_train &#061; Y(training(cv), :);<br \/>\nX_val &#061; X(test(cv), :); % 20%\u7684\u9a8c\u8bc1\u6570\u636e<br \/>\nY_val &#061; Y(test(cv), :);<\/p>\n<p>% \u786e\u4fdd Y_train \u548c Y_val \u662f\u6570\u503c\u7c7b\u578b<br \/>\nif iscategorical(Y_train)<br \/>\n    Y_train &#061; double(Y_train); % \u8f6c\u6362\u4e3a\u6570\u503c\u578b<br \/>\nend<br \/>\nif iscategorical(Y_val)<br \/>\n    Y_val &#061; double(Y_val); % \u8f6c\u6362\u4e3a\u6570\u503c\u578b<br \/>\nend<\/p>\n<p>% \u786e\u4fdd X_train \u548c X_test \u7684\u5217\u6570\u4e00\u81f4<br \/>\ndisp(size(X_train)); % \u6253\u5370 X_train \u7684\u5927\u5c0f<br \/>\ndisp(size(X_test));  % \u6253\u5370 X_test \u7684\u5927\u5c0f<\/p>\n<p>% \u5b9a\u4e49\u6a21\u578b\u540d\u79f0<br \/>\nmodelNames &#061; {&#039;Multinomial Logistic Regression&#039;, &#039;Decision Tree&#039;, &#039;Random Forest&#039;, &#8230;<br \/>\n              &#039;SVM&#039;, &#039;KNN&#039;, &#039;BP Neural Network&#039;, &#039;GBM&#039;, &#039;AdaBoost&#039;};<br \/>\nmodels &#061; cell(length(modelNames), 1); % \u7528\u4e8e\u5b58\u50a8\u6a21\u578b<br \/>\npredictions &#061; cell(length(modelNames), 1); % \u7528\u4e8e\u5b58\u50a8\u6d4b\u8bd5\u96c6\u7684\u9884\u6d4b\u7ed3\u679c<\/p>\n<p>% \u5faa\u73af\u904d\u5386\u6bcf\u4e2a\u6a21\u578b<br \/>\nfor i &#061; 1:length(modelNames)<br \/>\n    switch modelNames{i}<br \/>\n        case &#039;Multinomial Logistic Regression&#039;<br \/>\n            % \u591a\u5206\u7c7b\u903b\u8f91\u56de\u5f52<br \/>\n            B &#061; mnrfit(X_train, Y_train, &#039;model&#039;, &#039;nominal&#039;);<br \/>\n            predProb &#061; mnrval(B, X_test); % \u9884\u6d4b\u7c7b\u522b\u6982\u7387<br \/>\n            [~, predictions{i}] &#061; max(predProb, [], 2); % \u8fd4\u56de\u6982\u7387\u6700\u5927\u7c7b\u522b<\/p>\n<p>            % \u5728\u9a8c\u8bc1\u96c6\u4e0a\u8fdb\u884c\u9884\u6d4b<br \/>\n            predProb_val &#061; mnrval(B, X_val); % \u5728\u9a8c\u8bc1\u96c6\u4e0a\u8fdb\u884c\u9884\u6d4b<br \/>\n            [~, val_predictions] &#061; max(predProb_val, [], 2);<\/p>\n<p>        case &#039;Decision Tree&#039;<br \/>\n            % \u51b3\u7b56\u6811<br \/>\n            model &#061; fitctree(X_train, Y_train);<br \/>\n            predictions{i} &#061; predict(model, X_test);<br \/>\n            val_predictions &#061; predict(model, X_val); % \u5728\u9a8c\u8bc1\u96c6\u4e0a\u8fdb\u884c\u9884\u6d4b<\/p>\n<p>        case &#039;Random Forest&#039;<br \/>\n            % \u968f\u673a\u68ee\u6797<br \/>\n            model &#061; fitcensemble(X_train, Y_train, &#039;Method&#039;, &#039;Bag&#039;);<br \/>\n            predictions{i} &#061; predict(model, X_test);<br \/>\n            val_predictions &#061; predict(model, X_val); % \u5728\u9a8c\u8bc1\u96c6\u4e0a\u8fdb\u884c\u9884\u6d4b<\/p>\n<p>        case &#039;SVM&#039;<br \/>\n            % SVM \u7528\u4e8e\u591a\u5206\u7c7b<br \/>\n            model &#061; fitcecoc(X_train, Y_train); % \u4f7f\u7528 fitcecoc \u6765\u5904\u7406\u591a\u5206\u7c7b<br \/>\n            predictions{i} &#061; predict(model, X_test);<br \/>\n            val_predictions &#061; predict(model, X_val); % \u5728\u9a8c\u8bc1\u96c6\u4e0a\u8fdb\u884c\u9884\u6d4b<\/p>\n<p>        case &#039;KNN&#039;<br \/>\n            % K-\u8fd1\u90bb<br \/>\n            model &#061; fitcknn(X_train, Y_train);<br \/>\n            predictions{i} &#061; predict(model, X_test);<br \/>\n            val_predictions &#061; predict(model, X_val); % \u5728\u9a8c\u8bc1\u96c6\u4e0a\u8fdb\u884c\u9884\u6d4b<\/p>\n<p>        case &#039;BP Neural Network&#039;<br \/>\n            % BP \u795e\u7ecf\u7f51\u7edc<br \/>\n            model &#061; feedforwardnet(15); % 10\u4e3a\u9690\u85cf\u5c42\u795e\u7ecf\u5143\u4e2a\u6570<br \/>\n            model &#061; train(model, X_train&#039;, full(ind2vec(Y_train&#039;))); % ind2vec\u7528\u4e8e\u591a\u7c7b<br \/>\n            nnOutput &#061; model(X_test&#039;)&#039;; % \u8f93\u51fa\u795e\u7ecf\u7f51\u7edc\u9884\u6d4b\u7ed3\u679c<br \/>\n            [~, predictions{i}] &#061; max(nnOutput, [], 2); % \u9009\u62e9\u6982\u7387\u6700\u9ad8\u7684\u7c7b<br \/>\n            nnOutput_val &#061; model(X_val&#039;)&#039;; % \u5728\u9a8c\u8bc1\u96c6\u4e0a\u8fdb\u884c\u9884\u6d4b<br \/>\n            [~, val_predictions] &#061; max(nnOutput_val, [], 2); % \u9009\u62e9\u6982\u7387\u6700\u9ad8\u7684\u7c7b<\/p>\n<p>        case &#039;GBM&#039;<br \/>\n            % \u68af\u5ea6\u63d0\u5347&#xff08;GBM&#xff09; &#8211; \u4f7f\u7528\u9002\u5408\u591a\u5206\u7c7b\u7684 AdaBoostM2<br \/>\n            model &#061; fitcensemble(X_train, Y_train, &#039;Method&#039;, &#039;AdaBoostM2&#039;);<br \/>\n            predictions{i} &#061; predict(model, X_test);<br \/>\n            val_predictions &#061; predict(model, X_val); % \u5728\u9a8c\u8bc1\u96c6\u4e0a\u8fdb\u884c\u9884\u6d4b<\/p>\n<p>        case &#039;AdaBoost&#039;<br \/>\n            % AdaBoost<br \/>\n            model &#061; fitcensemble(X_train, Y_train, &#039;Method&#039;, &#039;AdaBoostM2&#039;);<br \/>\n            predictions{i} &#061; predict(model, X_test);<br \/>\n            val_predictions &#061; predict(model, X_val); % \u5728\u9a8c\u8bc1\u96c6\u4e0a\u8fdb\u884c\u9884\u6d4b<br \/>\n    end<\/p>\n<p>    % \u8f93\u51fa\u6bcf\u4e2a\u6a21\u578b\u7684\u9884\u6d4b\u7ed3\u679c\u4e3a Excel \u6587\u4ef6<br \/>\n    results &#061; table(predictions{i}, &#039;VariableNames&#039;, {&#039;Predictions&#039;});<br \/>\n    writetable(results, [&#039;XXX_&#039; modelNames{i} &#039;_trainend.xlsx&#039;]);<\/p>\n<p>    % \u5c06\u9884\u6d4b\u7c7b\u522b\u6807\u7b7e\u6dfb\u52a0\u5230\u6d4b\u8bd5\u96c6\u6700\u540e\u4e00\u5217<br \/>\n    testResults &#061; testData;<br \/>\n    testResults.Predictions &#061; predictions{i}; % \u6dfb\u52a0\u9884\u6d4b\u7ed3\u679c\u5217<br \/>\n    writetable(testResults, [&#039;XXX_&#039; modelNames{i} &#039;_test_with_predictions.xlsx&#039;]);<\/p>\n<p>    % \u5728\u9a8c\u8bc1\u96c6\u4e0a\u8bc4\u4f30\u6a21\u578b\u7684\u7cbe\u5ea6<br \/>\n    figure;<br \/>\n    cm &#061; confusionchart(Y_val, val_predictions); % \u4f7f\u7528Y_val\u8fdb\u884c\u6df7\u6dc6\u77e9\u9635\u8ba1\u7b97<br \/>\n    title([&#039;Confusion Matrix &#8211; &#039; modelNames{i} &#039; (Validation Set)&#039;]);<\/p>\n<p>    % \u8ba1\u7b97\u5e76\u663e\u793a\u7cbe\u5ea6<br \/>\n    accuracy &#061; sum(diag(cm.NormalizedValues)) \/ sum(cm.NormalizedValues(:));<br \/>\n    fprintf(&#039;%s Validation Accuracy: %.2f%%\\\\n&#039;, modelNames{i}, accuracy * 100);<\/p>\n<p>    % \u53ef\u89c6\u5316\u7cbe\u5ea6<br \/>\n    figure;<br \/>\n    bar(accuracy);<br \/>\n    title([&#039;Validation Accuracy of &#039; modelNames{i}]);<br \/>\n    xlabel(&#039;Model&#039;);<br \/>\n    ylabel(&#039;Accuracy&#039;);<br \/>\n    xticks(1);<br \/>\n    xticklabels({modelNames{i}});<br \/>\nend<\/p>\n<p>\u4ea7\u751f&#xff1a;<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"515\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250420230731-68057e333abc0.png\" width=\"632\" \/><\/p>\n<p>Multinomial Logistic Regression Validation Accuracy: 92.16% Decision Tree Validation Accuracy: 80.39% Random Forest Validation Accuracy: 90.20% SVM Validation Accuracy: 94.12% KNN Validation Accuracy: 92.16% BP Neural Network Validation Accuracy: 88.24% GBM Validation Accuracy: 84.31% AdaBoost Validation Accuracy: 84.31%<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb741\u6b21\uff0c\u70b9\u8d5e4\u6b21\uff0c\u6536\u85cf6\u6b21\u3002\u672c\u6587\u4f7f\u7528 MATLAB \u5b9e\u73b0\u4e86 8 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matlab<\/p>\n","protected":false},"author":2,"featured_media":31037,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[1284,50,2395,1482,2490,207,427],"topic":[],"class_list":["post-31038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-matlab","tag-50","tag-2395","tag-1482","tag-2490","tag-207","tag-427"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Matlab\u591a\u79cd\u7b97\u6cd5\u89e3\u51b3\u672a\u6765\u676fB\u7684\u591a\u5206\u7c7b\u95ee\u9898 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/31038.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" 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