MATLAB:如何在k-NN分类后找到混淆矩阵？

Question

我正在MATLAB上做一个模式识别项目。我在做费舍尔虹膜数据集。我编写了一些用于k-NN分类的代码，在完成分类之后，我想要计算混淆矩阵。我所有的尝试都失败了。因此，我请求你帮助我找到一种计算混淆矩阵C的方法。

守则的有关部分如下：

fold_number = 5;
indices = crossvalind('Kfold',species, fold_number);

val = 1:2:5; % for these small k values there will not be an important difference
         % regarding the cp ErrorRates. The difference is going to be
         % observable for val = 1:2:100, for example!!! But the
         % exercise asks only for k = 1,3,5.

err_arr = [];

for k=val

    cp = classperf(species); % Reinitialize the cp-structure!

    for i = 1:fold_number
        test = (indices == i); 
        train = ~test;
        class = knnclassify(meas(test,:),meas(train,:),species(train), k);
        %class = knnclassify(meas(test,2),meas(train,2),species(train), k); % To experiment only with the 2nd feature

        classperf(cp,class,test);
    end
    err_arr = [err_arr; cp.ErrorRate];
    fprintf('The k-NN classification error rate for k = %d is: %f\n', k,cp.ErrorRate);
end  

fprintf('\n The error array is: \n');
disp(err_arr);

我期待着阅读你的答案！

Answer 1

如果您可以访问统计信息和机器学习工具箱，您可以简单地使用混淆垫函数。如果将已知的分类(knownGroups)和known ( class在示例中给出的预测分类)作为输入，则返回混淆矩阵class：

[C] = confusionmat(knownGroups,class)