How to plot confusion matrix
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bayoishola20
el 20 de Oct. de 2014
Comentada: bayoishola20
el 4 de Nov. de 2014
I have performed my image segmentation using kmeans but need to get the confusion matrix. My image segmentation matrix for six(6) classes has numbers 1 to 6 in it which is perfect. On getting my trained classes BW_1,BW_2,BW_3,BW_4,BW_5,BW_6 I have in each only one's(1's) and zero's(0's) but need to create a single confusion matrix like in this link http://www.mathworks.com/help/nnet/ref/plotconfusion.html?searchHighlight=confusion%2520matrix
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Greg Heath
el 20 de Oct. de 2014
The function plotconfusion handles more than 2 classes. Replace the iris_dataset or simplecluster_dataset in the help and doc examples for plotconfusion
[x,t] = iris_dataset;
net = patternnet;
rng('default')
[net tr y e] = train(net,x,t);
NMSE = mse(e)/mean(var(t',1)) %0.0418
R2 = 1-NMSE %0.9582
plotconfusion(t,y);
Find the minimum number of hidden nodes that yields an acceptable result
Hope this helps.
Thank you for formally accepting my answer
Greg
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Greg Heath
el 21 de Oct. de 2014
That command assumes the input and target matrices are combined as in the MATLAB example database
help nndatasets
doc nndatasets
If your data is not formatted that way, then change the command to read the way your data is formatted.
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Star Strider
el 20 de Oct. de 2014
Editada: Star Strider
el 20 de Oct. de 2014
7 comentarios
Star Strider
el 20 de Oct. de 2014
If you have vectors with your known and predicted classes, those are your inputs to your confusion matrix. I got the impression from your Question, specifically ‘My image segmentation matrix for six(6) classes has numbers 1 to 6 in it which is perfect.’ that you already had those and simply wanted to know how to create a confusion matrix for your 6 classes.
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