How do I get the confusion matrix for all trained data
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Jon Cherrie on 3 May 2021
You can use the confusionmat command
When you call it, make sure that you pass the data from all sets that you want to combine.
For example, if you're using patternnet to train a network,
[x,t] = iris_dataset;
net = patternnet(10);
net = train(net,x,t);
y = net(x);
% Decode the predictions from "one hot" to classes
species = ["Setosa" "Versicolour" "Virginica"];
t = onehotdecode(t,species,1,"categorical");
y = onehotdecode(y,species,1,"categorical");
% Compute the confusion matrix and display -- note that 't' and 'y' contain
% results from all three sets
cm = confusionmat(t,y)
I would be careful about combining train, valiadation and test sets in this way as it might lead to misleading results.
Image Analyst on 2 May 2021
It should be pretty straightforward. Just determine the predicted class, and the actual class, where each class is identified by an integer. Then just increment the confusion matrix there
trueClass = 5; % Whatever
estimatedClass = 4; % For example
% Increment the count.
confusionMatrix(trueClass, estimatedClass) = confusionMatrix(trueClass, estimatedClass) + 1;
Note : you cannot determine a confusion matrix for test data since you don't know the true class - you only know the estimated class. You only know the true class for the training data and validation data, not the test data.