基于支持向量机SVM的Matlab二分类与多分类预测建模：直观注释，图形展示包括分类预测图、混淆矩阵图与ROC曲线图,利用支持向量机SVM进行二分类与多分类预测建模的Matlab程序详解,利用支持向量
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基于支持向量机SVM的Matlab二分类与多分类预测建模：直观注释，图形展示包括分类预测图、混淆矩阵图与ROC曲线图,利用支持向量机SVM进行二分类与多分类预测建模的Matlab程序详解,利用支持向量机SVM做二分类和多分类预测建模。程序注释详细直接替数据就可以用。程序语言为matlab。程序可以直接可以出分类预测图，混淆矩阵图，ROC曲线图。,SVM; 二分类预测建模; 多分类预测建模; 程序注释; MATLAB; 分类预测图; 混淆矩阵图; ROC曲线图。,基于SVM的二分类与多分类预测建模及可视化分析程序 <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/90425805/2/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/90425805/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">支持向量机（<span class="ff2">SVM</span>）二分类与多分类预测建模实践<span class="ff2">——</span>探秘<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _0"> </span></span>的世界</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">曾经在无数的算法世界里，<span class="_ _1"></span>我们探索着不同的分类模型，<span class="_ _1"></span>寻找着能够精准捕捉数据特性的算</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">法。<span class="_ _2"></span>今天<span class="_ _2"></span>，我<span class="_ _2"></span>们将<span class="_ _2"></span>深入<span class="_ _2"></span>探讨<span class="_ _2"></span>如何<span class="_ _2"></span>利用<span class="_ _2"></span>支持<span class="_ _2"></span>向量<span class="_ _2"></span>机（<span class="_ _2"></span><span class="ff2">SVM</span>）<span class="_ _2"></span>进行<span class="_ _2"></span>二分<span class="_ _2"></span>类和<span class="_ _2"></span>多分<span class="_ _2"></span>类预<span class="_ _2"></span>测建<span class="_ _2"></span>模，</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">以<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _0"> </span></span>为工具，为大家展示一个真实而丰富的机器学习实践案例。</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">一、引言</div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">在当今<span class="_ _2"></span>的大数据<span class="_ _2"></span>时代，<span class="_ _2"></span><span class="ff2">SVM<span class="_"> </span></span>作为一种强大<span class="_ _2"></span>的监督<span class="_ _2"></span>学习算法<span class="_ _2"></span>，在二分<span class="_ _2"></span>类和多<span class="_ _2"></span>分类问题<span class="_ _2"></span>中都有</div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">着广泛<span class="_ _2"></span>的应用。<span class="_ _2"></span>无论是<span class="_ _2"></span>图像识别<span class="_ _2"></span>、文本分<span class="_ _2"></span>类还是<span class="_ _2"></span>生物信息<span class="_ _2"></span>学等领<span class="_ _2"></span>域，<span class="ff2">SVM<span class="_"> </span></span>都能展现<span class="_ _2"></span>出其独</div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">特的优势。</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">二、<span class="ff2">SVM<span class="_"> </span></span>二分类预测建模</div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">在<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _0"> </span></span>中，<span class="_ _3"></span>我们可以使用内置的<span class="_ _0"> </span><span class="ff2">SVM<span class="_ _0"> </span></span>模型进行二分类预测。<span class="_ _3"></span>首先，<span class="_ _3"></span>我们需要准备数据集，</div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">并进行必要的预处理。然后，我们<span class="_ _2"></span>可以利用<span class="_ _0"> </span><span class="ff2">SVM<span class="_"> </span></span>算法对数据进行训练，并生成一个模型。</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">以下是简单的<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _0"> </span></span>代码示例：</div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">```matlab</div><div class="t m0 x1 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _4"> </span><span class="ff1">加载数据集（这里以鸢尾花数据集为例）</span></div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">load fisheriris</div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">X = meas; % <span class="_ _4"> </span><span class="ff1">特征值</span></div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">y = species; % <span class="_ _4"> </span><span class="ff1">标签值（这里仅作为示例，应替换为实际数据）</span></div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _4"> </span><span class="ff1">将数据集分为训练集和测试集</span></div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">cv = cvpartition(y, 'Holdout', 0.3); % 70%<span class="ff1">训练，</span>30%<span class="ff1">测试</span></div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">XTrain = X(cv.training,:); yTrain = y(cv.training);</div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">XTest = X(cv.test,:); yTest = y(cv.test);</div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _4"> </span><span class="ff1">使用<span class="_ _0"> </span></span>SVM<span class="_ _0"> </span><span class="ff1">进行训练</span></div><div class="t m0 x1 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0">SVMModel = fitcsvm(XTrain, yTrain);</div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _4"> </span><span class="ff1">预测与评估</span></div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">[yFitted,scores] = predict(SVMModel, XTest);</div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">confusionmat(yTest, yFitted) % <span class="_ _4"> </span><span class="ff1">混淆矩阵图（若要展示为图表请保存至<span class="_ _0"> </span></span>figure<span class="_ _4"> </span><span class="ff1">对象）</span></div><div class="t m0 x1 h2 y1b ff2 fs0 fc0 sc0 ls0 ws0">roc(SVMModel, XTest, yTest) % ROC<span class="_ _0"> </span><span class="ff1">曲线图（类似操作需自行封装）</span></div><div class="t m0 x1 h2 y1c ff2 fs0 fc0 sc0 ls0 ws0">```</div><div class="t m0 x1 h2 y1d ff1 fs0 fc0 sc0 ls0 ws0">通过以上代码，<span class="_ _5"></span>我们能够完成一个基本的二分类预测建模任务。<span class="_ _5"></span>对于复杂的数据集，<span class="_ _5"></span>可能需</div><div class="t m0 x1 h2 y1e ff1 fs0 fc0 sc0 ls0 ws0">要进行更复杂的预处理和参数调整。</div><div class="t m0 x1 h2 y1f ff1 fs0 fc0 sc0 ls0 ws0">三、<span class="ff2">SVM<span class="_"> </span></span>多分类预测建模</div><div class="t m0 x1 h2 y20 ff1 fs0 fc0 sc0 ls0 ws0">对于多分类问题，<span class="_ _6"></span>我们同样可以使用<span class="_ _0"> </span><span class="ff2">SVM<span class="_ _0"> </span></span>进行处理。<span class="_ _6"></span><span class="ff2">Matlab<span class="_ _4"> </span><span class="ff1">提供了多种多分类策略供我们</span></span></div><div class="t m0 x1 h2 y21 ff1 fs0 fc0 sc0 ls0 ws0">选择。以下是一个简单的多分类<span class="_ _0"> </span><span class="ff2">SVM<span class="_ _0"> </span></span>模型的代码示例：</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>