如何在java中利用RandomForest实现一个随机森林效果

如何在java中利用RandomForest实现一个随机森林效果？相信很多没有经验的人对此束手无策，为此本文总结了问题出现的原因和解决方法，通过这篇文章希望你能解决这个问题。

随机森林是由多棵树组成的分类或回归方法。主要思想来源于Bagging算法，Bagging技术思想主要是给定一弱分类器及训练集，让该学习算法训练多轮，每轮的训练集由原始训练集中有放回的随机抽取，大小一般跟原始训练集相当，这样依次训练多个弱分类器，最终的分类由这些弱分类器组合，对于分类问题一般采用多数投票法，对于回归问题一般采用简单平均法。随机森林在bagging的基础上，每个弱分类器都是决策树，决策树的生成过程中中，在属性的选择上增加了依一定概率选择属性，在这些属性中选择最佳属性及分割点，传统做法一般是全部属性中去选择最佳属性，这样随机森林有了样本选择的随机性，属性选择的随机性，这样一来增加了每个分类器的差异性、不稳定性及一定程度上避免每个分类器的过拟合（一般决策树有过拟合现象），由此组合分类器增加了最终的泛化能力。下面是代码的简单实现

public class RandomForest {  List<Sample> mSamples;  List<Cart> mCarts;  double mFeatureRate;  int mMaxDepth;  int mMinLeaf;  Random mRandom;    public void loadData(String path,String regex) throws Exception{    mSamples = new ArrayList<Sample>();    BufferedReader reader = new BufferedReader(new FileReader(path));    String line = null;    String splits[] = null;    Sample sample = null;    while(null != (line=reader.readLine())){      splits = line.split(regex);      sample = new Sample();      sample.label = Double.valueOf(splits[0]);      sample.feature = new ArrayList<Double>(splits.length-1);      for(int i=0;i<splits.length-1;i++){        sample.feature.add(new Double(splits[i+1]));      }      mSamples.add(sample);    }    reader.close();  }  public void train(int iters){    mCarts = new ArrayList<Cart>(iters);    Cart cart = null;    for(int iter=0;iter<iters;iter++){      cart = new Cart();      cart.mFeatureRate = mFeatureRate;      cart.mMaxDepth = mMaxDepth;      cart.mMinLeaf = mMinLeaf;      cart.mRandom = mRandom;      List<Sample> s = new ArrayList<Sample>(mSamples.size());      for(int i=0;i<mSamples.size();i++){        s.add(mSamples.get(cart.mRandom.nextInt(mSamples.size())));      }      cart.setData(s);      cart.train();      mCarts.add(cart);      System.out.println("iter: "+iter);      s = null;    }  }    public double classify(Sample sample){    double val = 0;    for(Cart cart:mCarts){      val += cart.classify(sample);    }    return val/mCarts.size();  }    public static void main(String[] args) throws Exception {    // TODO Auto-generated method stub    RandomForest forest = new RandomForest();    forest.loadData("F:/2016-contest/20161001/train_data_1.csv", ",");    forest.mFeatureRate = 0.8;    forest.mMaxDepth = 3;    forest.mMinLeaf = 1;    forest.mRandom = new Random();    forest.mRandom.setSeed(100);    forest.train(100);        List<Sample> samples = Cart.loadTestData("F:/2016-contest/20161001/valid_data_1.csv", true, ",");    double sum = 0;    for(Sample s:samples){      double val = forest.classify(s);      sum += (val-s.label)*(val-s.label);      System.out.println(val+" "+s.label);    }    System.out.println(sum/samples.size()+" "+sum);    System.out.println(System.currentTimeMillis());  }}