机器学习-采用决策树对wine分类

1.数据集的准备

我采用UCI中的Wine Data Set

下载地址：http://download.csdn.net/download/tiankong_/10120450

数据描述:

第一列为类属性 ，用1,2,3表示，后面13列为特征属性，分别为Alcohol，Malicacid，Ash，Alcalinity of ash，Magnesium，Total phenols，Flavanoids，Nonflavanoid phenols，Proanthocyanins，Color intensity，Hue，OD280/OD315 of diluted wines，Proline

2.代码实例

#include "opencv2/ml/ml.hpp"  #include "opencv2/core/core.hpp"  #include "opencv2/core/utility.hpp"  #include <stdio.h>  #include <string>  #include <map>  #include <vector>  #include<iostream>  using namespace std;using namespace cv;using namespace cv::ml;static void help(){printf("\nThis sample demonstrates how to use different decision trees and forests including boosting and random trees.\n""Usage:\n\t./tree_engine [-r <response_column>] [-ts type_spec] <csv filename>\n""where -r <response_column> specified the 0-based index of the response (0 by default)\n""-ts specifies the var type spec in the form ord[n1,n2-n3,n4-n5,...]cat[m1-m2,m3,m4-m5,...]\n""<csv filename> is the name of training data file in comma-separated value format\n\n");}static void train_and_print_errs(Ptr<StatModel> model, const Ptr<TrainData>& data){bool ok = model->train(data);if (!ok){printf("Training failed\n");}else{printf("train error: %f\n", model->calcError(data, false, noArray()));printf("test error: %f\n\n", model->calcError(data, true, noArray()));}}int main(int argc, char** argv){if (argc < 2){help();return 0;}const char* filename = 0;int response_idx = 0;std::string typespec;for (int i = 1; i < argc; i++){if (strcmp(argv[i], "-r") == 0)sscanf(argv[++i], "%d", &response_idx);else if (strcmp(argv[i], "-ts") == 0)typespec = argv[++i];else if (argv[i][0] != '-')filename = argv[i];else{printf("Error. Invalid option %s\n", argv[i]);help();return -1;}}printf("\nReading in %s...\n\n", filename);const double train_test_split_ratio = 0.5;//加载训练数据  Ptr<TrainData> data = TrainData::loadFromCSV(filename, 0, response_idx, response_idx + 1, typespec);if (data.empty()){printf("ERROR: File %s can not be read\n", filename);return 0;}data->setTrainTestSplitRatio(train_test_split_ratio);//预测数据  float test1[] = { 14.23, 1.71, 2.43, 15.6, 127, 2.8, 3.06, .28, 2.29, 5.64, 1.04, 3.92, 1065 };float test2[] = { 12.37, .94, 1.36, 10.6, 88, 1.98, .57, .28, .42, 1.95, 1.05, 1.82, 520 };float test3[] = { 12.86, 1.35, 2.32, 18, 122, 1.51, 1.25, .21, .94, 4.1, .76, 1.29, 630 };Mat test1Map(1, 13, CV_32FC1, test1);Mat test2Map(1, 13, CV_32FC1, test2);Mat test3Map(1, 13, CV_32FC1, test3);printf("======DTREE=====\n");  //创建决策树  Ptr<DTrees> dtree = DTrees::create();  dtree->setMaxDepth(10);    //设置决策树的最大深度  dtree->setMinSampleCount(2);  //设置决策树叶子节点的最小样本数  dtree->setRegressionAccuracy(0);  //设置回归精度  dtree->setUseSurrogates(false);   //不使用替代分叉属性  dtree->setMaxCategories(16);   //设置最大的类数量  dtree->setCVFolds(0);  //设置不交叉验证  dtree->setUse1SERule(false);  //不使用1SE规则  dtree->setTruncatePrunedTree(false);  //不对分支进行修剪  dtree->setPriors(Mat());  //设置先验概率  train_and_print_errs(dtree, data);  dtree->save("dtree_result.xml");  //读取模型，强行使用一下，为了强调这种用法，当然此处完全没必要  Ptr<DTrees> dtree2 = DTrees::load<DTrees>("dtree_result.xml");  cout << dtree2->predict(test1Map) << endl;  cout << dtree2->predict(test2Map) << endl;  cout << dtree2->predict(test3Map) << endl;  cout << "============================================" << endl;  return 0;}