Decision Tree的Matlab实现和原理

    clear all;
    clc;
    [S1,S2,S3,S4,S5,S6,S7,S8,classity]=textread('Pima-training-set.txt','%f %f %f %f %f %f %f %f %s');%Pima-training-set.txt
    D=[S1 S2 S3 S4 S5 S6 S7 S8];
    AttributName={ 'preg','plas','pres','skin','insu','mass','pedi','age'};
    t=classregtree(D,classity,'names',AttributName);
    t=prune(t,'level',1);% j减掉最后5层
    view(t);
    yfit1=eval(t,D,1);
    count1=0;
    for i=1:length(yfit1)
    if(strcmp(classity(i,1),yfit1(i,1)))
           count1=count1+1;
    end
    end
    fprintf('Accuracy of training is:%d \n',count1/length(yfit1));
    
    costsum=zeros