Is there a way to plot a confusion matrix of the cross validation results?
6 Ansichten (letzte 30 Tage)
Ältere Kommentare anzeigen
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
2 Kommentare
ROHAN JAIN
am 30 Jun. 2020
Bearbeitet: ROHAN JAIN
am 30 Jun. 2020
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
Antworten (2)
Santhana Raj
am 11 Mai 2017
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.
0 Kommentare
Siehe auch
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!