Plot confusion doesn't work
显示 更早的评论
Hello,
I have the problem that when I try use plotconfusion, this function doesn't work.
I have dataset with 15 classes and I try to predict the target value using knn-classification. I've divided datasets to training and test datasets (75:25 accordinaly). My dataset has 300 instances and 90 attributes.
The problem is that when I try to call this plotconfusion function I just see that this doesn't work (it somehow just go to a infinite cycle or something like this, the process doesn't terminate). Could you tell me what's the problem or do I use it wrong?
Here the part of my code: knn = ClassificationKNN.fit(XtrainNN,YtrainNN,'NumNeighbors',5); Y_knn = knn.predict(XtestNN); loss(knn, XtestNN, YtestNN) plotconfusion(Y_knn,YtestNN)
3 个评论
Andrew Singh
2017-9-20
I'm also having the same problem.
Baran Yildiz
2017-9-26
I am also having the same problem. Plot confusion doesn't seem to work even for the sample problem/dataset given in the reference link below:
https://au.mathworks.com/help/nnet/ref/plotconfusion.html#inputarg_targets
Tapan
2023-8-11
My error is please tell me how to solve
Error using plotconfusion>standard_args (line 255) Value is not a matrix or cell array.
Error in plotconfusion (line 111) update_args = standard_args(args{:});
Error in dltt (line 18) plotconfusion(testdata.Labels, Predicted);
回答（3 个）
Nathan DeJong
2017-9-27
6 个投票
Try transposing the inputs so that they are row vectors rather than column vectors. It worked for me. Seems to be a strange bug in plotconfusion().
1 个评论
Pedro Borges
2018-10-17
transposing the inputs worked for me, too! thanks!
Hamid Salimi
2021-6-9
2 个投票
I write it for anyone that may have the same problem, I solved it by converting my actual and predicted results to categorical data! your actual and predicted should be n * 1, and then use it:
plotconfusion(categorical(actual),categorical(predicted));
Ilya
2013-12-17
I never used plotconfusion, but you can get what you want using functions confusionmat and imagesc. For example,
knn = ClassificationKNN.fit(XtrainNN,YtrainNN,'NumNeighbors',5);
Y_knn = knn.predict(XtestNN);
cm = confusionmat(YtestNN,Y_knn);
imagesc(cm);
colorbar;
类别
在 帮助中心 和 File Exchange 中查找有关 Support Vector Machine Regression 的更多信息
2013-12-17
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!
Translated by 
