[c] = confusion(t,y) and not classified observations
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Hi
[c] = confusion(t,y)
[c] its Confusion value = fraction of samples misclassified
but output of code bellow can be values:
1)classified correctly
2)classified incorrectly
3)not classified
net = fitnet(hl,trainFcn)
[net,tr] = train(net,x,t);
y = net(x)
[c] = confusion(t,y)
if im getting c = 0 it mean all values were classified correctly but it will consider values which were not classified ?
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Srivardhan Gadila
am 28 Mär. 2020
Bearbeitet: Srivardhan Gadila
am 28 Mär. 2020
The fitnet function is used for regression. The confusion matrix is used for classification problems.
Function fitting is the process of training a neural network on a set of inputs in order to produce an associated set of target outputs. After you construct the network with the desired hidden layers and the training algorithm, you must train it using a set of training data. Once the neural network has fit the data, it forms a generalization of the input-output relationship. You can then use the trained network to generate outputs for inputs it was not trained on.
3 Kommentare
Srivardhan Gadila
am 29 Mär. 2020
@Tomasz Kaczmarski, Try the following in MATLAB:
help simpleclass_dataset
[x,t] = simpleclass_dataset;
plot(x(1,:),x(2,:),'+')
net = patternnet(10);
net = train(net,x,t);
view(net)
y = net(x)
plotconfusion(t,y)
% or use the below one
[c,cm,ind,per] = confusion(t,y)
Tomasz Kaczmarski
am 30 Mär. 2020
Bearbeitet: Tomasz Kaczmarski
am 30 Mär. 2020
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