場景：一般分類預測直接輸出類別標記，不過有些情況需要輸出對應類別的概率值，比如判定為正例的概率是0.6，而判定為負例的概率是0.3，那自然標記為正例，這里就是看ML用classDistribution輸出各類別的概率值。參考代碼如下：

/*** This file is part of the Java Machine Learning Library* * The Java Machine Learning Library is free software; you can redistribute it and/or modify* it under the terms of the GNU General Public License as published by* the Free Software Foundation; either version 2 of the License, or* (at your option) any later version.* * The Java Machine Learning Library is distributed in the hope that it will be useful,* but WITHOUT ANY WARRANTY; without even the implied warranty of* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the* GNU General Public License for more details.* * You should have received a copy of the GNU General Public License* along with the Java Machine Learning Library; if not, write to the Free Software* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA* * Copyright (c) 2006-2012, Thomas Abeel* * Project: http://java-ml.sourceforge.net/* */ package com.gddx;import java.io.File; import java.util.Map; import java.util.Random;import be.abeel.util.Pair; import net.sf.javaml.classification.Classifier; import net.sf.javaml.classification.tree.RandomForest; import net.sf.javaml.core.Dataset; import net.sf.javaml.core.DenseInstance; import net.sf.javaml.core.Instance; import net.sf.javaml.sampling.Sampling; import net.sf.javaml.tools.data.FileHandler;/*** Tutorial for the random forest classifier.* * @author Thomas Abeel* */ public class TutorialRandomForest {/*** Shows the default usage of the random forest algorithm.*/public static void main(String[] args) throws Exception {/* Load a data set */Dataset ori_data = FileHandler.loadDataset(new File("D:\\tmp\\javaml-0.1.7-src\\UCI-small\\iris\\iris.data"), 4, ",");Sampling s = Sampling.SubSampling;Pair<Dataset, Dataset> sam_data = s.sample(ori_data, (int) (ori_data.size() * 0.8));/** Contruct a RF classifier that uses 5 neighbors to make a decision.*/Classifier rf = new RandomForest(50, false, 3, new Random());rf.buildClassifier(sam_data.x());//80%樣本訓練/* 輸出預測的類別概率 */for(Instance inst:sam_data.y()){ //20%樣本驗證Map<Object,Double> mprob=rf.classDistribution(inst);//輸出類別的概率，[0,1]System.out.println(mprob); }}}
執行結果：

{Iris-virginica=0.0, Iris-setosa=1.0000000000000004, Iris-versicolor=0.0} {Iris-virginica=0.0, Iris-setosa=1.0000000000000004, Iris-versicolor=0.0} {Iris-virginica=0.0, Iris-setosa=1.0000000000000004, Iris-versicolor=0.0} {Iris-virginica=0.0, Iris-setosa=1.0000000000000004, Iris-versicolor=0.0} {Iris-virginica=0.0, Iris-setosa=1.0000000000000004, Iris-versicolor=0.0} {Iris-virginica=0.0, Iris-setosa=1.0000000000000004, Iris-versicolor=0.0} {Iris-virginica=0.0, Iris-setosa=1.0000000000000004, Iris-versicolor=0.0} {Iris-virginica=0.0, Iris-setosa=1.0000000000000004, Iris-versicolor=0.0} {Iris-virginica=0.0, Iris-setosa=1.0000000000000004, Iris-versicolor=0.0} {Iris-virginica=0.0, Iris-setosa=1.0000000000000004, Iris-versicolor=0.0} {Iris-virginica=0.0, Iris-setosa=0.7400000000000003, Iris-versicolor=0.25999999999999995} {Iris-virginica=0.0, Iris-setosa=1.0000000000000004, Iris-versicolor=0.0} {Iris-virginica=0.0, Iris-setosa=0.0, Iris-versicolor=1.0000000000000004} {Iris-virginica=0.0, Iris-setosa=0.0, Iris-versicolor=1.0000000000000004} {Iris-virginica=0.0, Iris-setosa=0.0, Iris-versicolor=1.0000000000000004} {Iris-virginica=0.08, Iris-setosa=0.0, Iris-versicolor=0.9200000000000005} {Iris-virginica=0.46000000000000013, Iris-setosa=0.0, Iris-versicolor=0.5400000000000001} {Iris-virginica=0.0, Iris-setosa=0.0, Iris-versicolor=1.0000000000000004} {Iris-virginica=0.0, Iris-setosa=0.0, Iris-versicolor=1.0000000000000004} {Iris-virginica=0.0, Iris-setosa=0.0, Iris-versicolor=1.0000000000000004} {Iris-virginica=0.0, Iris-setosa=0.0, Iris-versicolor=1.0000000000000004} {Iris-virginica=1.0000000000000004, Iris-setosa=0.0, Iris-versicolor=0.0} {Iris-virginica=1.0000000000000004, Iris-setosa=0.0, Iris-versicolor=0.0} {Iris-virginica=0.32, Iris-setosa=0.0, Iris-versicolor=0.6800000000000003} {Iris-virginica=1.0000000000000004, Iris-setosa=0.0, Iris-versicolor=0.0} {Iris-virginica=1.0000000000000004, Iris-setosa=0.0, Iris-versicolor=0.0} {Iris-virginica=1.0000000000000004, Iris-setosa=0.0, Iris-versicolor=0.0} {Iris-virginica=1.0000000000000004, Iris-setosa=0.0, Iris-versicolor=0.0} {Iris-virginica=1.0000000000000004, Iris-setosa=0.0, Iris-versicolor=0.0} {Iris-virginica=0.9000000000000005, Iris-setosa=0.0, Iris-versicolor=0.1}

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