Is there a way to plot a confusion matrix of the cross validation results?

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Hadeer tawfik 2016-10-30

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编辑： ROHAN JAIN 2020-6-30

Can somebody tell me how to plot a confusion matrix of the crossval result?

 CVMdl = crossval(classifier,'HoldOut',0.08);
  k=kfoldLoss(CVMdl,'lossFun','classiferror','mode','average')
  L = resubLoss(classifier,'LossFun','classiferror')
   Accuracy = 1 - k

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ben dp 2017-5-10

Hey Hadeer!

Did you find a solution? I'm in the same problem.

ROHAN JAIN 2020-6-30

编辑：ROHAN JAIN 2020-6-30

在 MATLAB Online 中打开

Hi,

You can plot the confusion matrix easily by using the following function:

confusionchart(testlabels,labels_predicted)

where testlabels are the labels of the test set and labels_predicted refers to the labels that have been predicted by the LDA classifier using predict().

It automatically plots the confusion matrix. Further, you can also store it in a variable and access the values using the dot operator as mentioned below.

cvmat=confusionchart(testlabels,labels_predicted)
cval=cmat.NormalizedValues; % cval is the required matrix

Hope it helps!

Thanks

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Answer 1

Santhana Raj 2017-5-11

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I am not aware of any method to plot confusion matrix. But usually I calculate the precision and recall from the true positives and true negatives. Some places I also use F-measure. Depending on your application, any of this might be a good measure to evaluate your classification algorithm.

Check wiki for the formulas for these.