AOA算法优化随机森林RF树数与叶子节点的拟合预测建模：详细注释的Matlab程序,算数优化算法AOA改良随机森林RF建模：多输入单输出预测，详细注释的Matlab程序，可视化评估与指标打印 ,算数优
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AOA算法优化随机森林RF树数与叶子节点的拟合预测建模：详细注释的Matlab程序,算数优化算法AOA改良随机森林RF建模：多输入单输出预测，详细注释的Matlab程序，可视化评估与指标打印。,算数优化算法AOA优化随机森林RF的树数和最小叶子数，建立多输入单输出的拟合预测建模。程序内注释详细，可学习性强，直接替数据可用。程序语言为matlab。直接运行可以出拟合预测图，优化迭代图，特征变量重要性排序图，可以打印R方，MSE，RMSE，MAPE等多种评价指标，便于分析。,算数优化算法AOA; 随机森林RF; 树数和最小叶子数优化; 拟合预测建模; 程序内注释详细; 直接运行出图; 评价指标,基于AOA算法优化的随机森林模型：多图可视化与性能评估系统 <link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90427205/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90427205/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">以下是一篇<span class="_ _0"> </span><span class="ff2">Matlab<span class="_"> </span></span>程序代码和相关的注释，用于建立多输入单输出的拟合预<span class="_ _1"></span>测模型，同时</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">使用<span class="_ _1"></span>了算<span class="_ _1"></span>数优<span class="_ _1"></span>化算<span class="_ _1"></span>法（<span class="_ _1"></span><span class="ff2">AOA</span>）<span class="_ _1"></span>来优<span class="_ _1"></span>化随<span class="_ _1"></span>机<span class="_ _1"></span>森林<span class="_ _1"></span>（<span class="ff2">RF<span class="_ _1"></span></span>）的<span class="_ _1"></span>树数<span class="_ _1"></span>和最<span class="_ _1"></span>小叶<span class="_ _1"></span>子数<span class="_ _1"></span>。程<span class="_ _1"></span>序包<span class="_ _1"></span>括<span class="_ _1"></span>拟合</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">预测图、<span class="_ _2"></span>优化迭代图、<span class="_ _2"></span>特征变量重要性排序图，<span class="_ _2"></span>并打印了<span class="_ _0"> </span><span class="ff2">R<span class="_ _0"> </span></span>方、<span class="_ _2"></span><span class="ff2">MSE<span class="ff1">、<span class="_ _2"></span><span class="ff2">RMSE<span class="_ _3"> </span><span class="ff1">和<span class="_ _3"> </span></span>MAPE<span class="_"> </span><span class="ff1">等多</span></span></span></span></div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">种评价指标。</div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">```matlab</div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _3"> </span><span class="ff1">导入数据</span></div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _3"> </span><span class="ff1">假设你的数据集已经准备好，并且已经加载到变量<span class="_ _3"> </span></span>data<span class="_"> </span><span class="ff1">中</span></div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">% data = load('your_data_file.csv'); % <span class="_ _3"> </span><span class="ff1">示例加载数据，请根据实际情况替换</span></div><div class="t m0 x1 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _3"> </span><span class="ff1">划分训练集和测试集</span></div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">cvp = cvpartition(data.response, 'Holdout', 0.2); % <span class="_ _3"> </span><span class="ff1">划分<span class="_ _3"> </span></span>80%<span class="ff1">的数据为训练集，</span>20%<span class="ff1">为测试集</span></div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">trainData = data(training(cvp), :); % <span class="_ _3"> </span><span class="ff1">训练集数据</span></div><div class="t m0 x1 h2 yc ff2 fs0 fc0 sc0 ls0 ws0">testData = data(test(cvp), :); % <span class="_ _3"> </span><span class="ff1">测试集数据</span></div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _3"> </span><span class="ff1">定义随机森林模型参数</span></div><div class="t m0 x1 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">numTrees = 100; % <span class="_ _3"> </span><span class="ff1">树的数量</span></div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">minLeafSize = 5; % <span class="_ _3"> </span><span class="ff1">最小叶子节点数</span></div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _3"> </span><span class="ff1">使用<span class="_ _3"> </span></span>AOA<span class="_"> </span><span class="ff1">算法优化参数</span></div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">function optimizedParams = AOA_optimizeRF(trainData, numTrees, minLeafSize)</div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>% <span class="_ _3"> </span><span class="ff1">初始化<span class="_ _3"> </span></span>AOA<span class="_"> </span><span class="ff1">参数（这需要根据具体情况设置，此处只是示例）</span></div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>initialParams = [numTrees, minLeafSize];</div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>...</div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>% <span class="_ _3"> </span><span class="ff1">这里编写<span class="_ _3"> </span></span>AOA<span class="_"> </span><span class="ff1">算法的核心部分，以寻找最优的树数和最小叶子数组合</span></div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>...</div><div class="t m0 x1 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>return optimizedParams; % <span class="_ _3"> </span><span class="ff1">返回最优的树数和最小叶子数组合</span></div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">end</div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _3"> </span><span class="ff1">调用<span class="_ _3"> </span></span>AOA<span class="_"> </span><span class="ff1">算法优化随机森林参数</span></div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">[optimizedNumTrees, <span class="_ _5"> </span>optimizedMinLeafSize] <span class="_ _5"> </span>= <span class="_ _5"> </span>AOA_optimizeRF(trainData, <span class="_ _5"> </span>numTrees, </div><div class="t m0 x1 h2 y1b ff2 fs0 fc0 sc0 ls0 ws0">minLeafSize);</div><div class="t m0 x1 h2 y1c ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _3"> </span><span class="ff1">建立随机森林模型并训练</span></div><div class="t m0 x1 h2 y1d ff2 fs0 fc0 sc0 ls0 ws0">rfModel <span class="_ _6"> </span>= <span class="_ _6"> </span>fitensemble(trainData.X, <span class="_ _6"> </span>trainData.Y, <span class="_ _6"> </span>'TreeBagger', <span class="_ _6"> </span>'NumTrees', </div><div class="t m0 x1 h2 y1e ff2 fs0 fc0 sc0 ls0 ws0">optimizedNumTrees, 'MinLeafSize', optimizedMinLeafSize);</div><div class="t m0 x1 h2 y1f ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _3"> </span><span class="ff1">拟合预测建模及评价指标计算</span></div><div class="t m0 x1 h2 y20 ff2 fs0 fc0 sc0 ls0 ws0">predictions = predict(rfModel, testData.X); % <span class="_ _3"> </span><span class="ff1">对测试集进行预测</span></div><div class="t m0 x1 h2 y21 ff2 fs0 fc0 sc0 ls0 ws0">R2 = r2(testData.Y, predictions); % R<span class="_ _3"> </span><span class="ff1">方计算</span></div><div class="t m0 x1 h2 y22 ff2 fs0 fc0 sc0 ls0 ws0">MSE = mean((testData.Y - predictions).^2); % <span class="_ _3"> </span><span class="ff1">均方误差<span class="_ _3"> </span></span>MSE<span class="_"> </span><span class="ff1">计算</span></div><div class="t m0 x1 h2 y23 ff2 fs0 fc0 sc0 ls0 ws0">RMSE = sqrt(MSE); % <span class="_ _3"> </span><span class="ff1">均方根误差<span class="_ _3"> </span></span>RMSE<span class="_"> </span><span class="ff1">计算</span></div><div class="t m0 x1 h2 y24 ff2 fs0 fc0 sc0 ls0 ws0">MAPE <span class="_ _7"></span>= <span class="_ _7"></span>mean(abs((testData.Y <span class="_ _7"></span>- <span class="_ _7"></span>predictions) <span class="_ _7"></span>./ <span class="_ _7"></span>testData.Y) <span class="_ _7"></span>* <span class="_ _7"></span>100); <span class="_ _7"></span>% <span class="_"> </span><span class="ff1">平<span class="_ _7"></span>均<span class="_ _7"></span>绝<span class="_ _7"></span>对<span class="_ _7"></span>百<span class="_ _7"></span>分<span class="_ _7"></span>比<span class="_ _7"></span>误<span class="_ _7"></span>差</span></div><div class="t m0 x1 h2 y25 ff2 fs0 fc0 sc0 ls0 ws0">MAPE<span class="_ _3"> </span><span class="ff1">计算</span></div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>