how to compare the class of input and output,and display the misclassification,how much percentage it is classified properly

Question

shruti 2014-9-29

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/156694-how-to-compare-the-class-of-input-and-output-and-display-the-misclassification-how-much-percentage-i

回答： Ahmed 2014-9-30

     x1        x2      class
 a= -1.7986   -1.6730    1.0000
   -1.0791   -0.5937    1.0000
   -0.5995    0.7556    1.0000
    1.0791   -1.4032    1.0000
    0.1199    0.2159    1.0000
    0.3597    0.4857   -1.0000
   -0.3597    1.5651   -1.0000
    0.5995    0.4857   -1.0000
    0.1199   -0.3238   -1.0000
    1.5588    0.4857   -1.0000
result=x1       x2     wx-gamma    class
   -1.7986   -1.6730    0.8068    1.0000
   -1.0791   -0.5937    0.3781    1.0000
   -0.5995    0.7556   -0.0706   -1.0000
    1.0791   -1.4032    0.1382    1.0000
    0.1199    0.2159   -0.0808   -1.0000
    0.3597    0.4857   -0.2004   -1.0000
   -0.3597    1.5651   -0.3298   -1.0000
    0.5995    0.4857   -0.2503   -1.0000
    0.1199   -0.3238    0.0588    1.0000
    1.5588    0.4857   -0.4500   -1.0000

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Answer 1

Ahmed 2014-9-30

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https://ww2.mathworks.cn/matlabcentral/answers/156694-how-to-compare-the-class-of-input-and-output-and-display-the-misclassification-how-much-percentage-i#answer_153351

在 MATLAB Online 中打开

To just get the accuracy it is only required to count the number of matches and divide by the total number of observations:

acc = sum(a.class == result.class)/size(a.class,1),

However, you should consider having a look at the confusion matrix as well:

cfMat = confusionmat(a.class,result.class),
acc   = sum(diag(cfMat))/sum(cfMat(:)),

Then print the result nicely:

fprintf('Accuracy: %.1f%%\n',100*acc);

In addition, investigating some sort of performance curve is also helpful:

[FPR,TPR,~,AUC]     = perfcurve(a.class, result.wx_gamma,1);
plot(FPR,TPR);
axis('equal'); 
axis([0 1 0 1]);
hold on; grid on;
line([0 1],[0 1]);
hold off;
xlabel('FPR'); ylabel('TPR');