# How can I plot a confusion matrix for a multi-class or non-binary classification problem?

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MathWorks Support Team il 1 Mag 2017
I want to make a plot similar to the confusion matrix created in the Classification Learner app. This can make a confusion matrix for a multi-class or non-binary classification problem. In addition, it can plot things such as a True Positive or False Negative rates.
How can I do this?

### Risposta accettata

MathWorks Support Team il 5 Lug 2017
Similar to the binary or two-class problem, this can be done using the "plotconfusion" function. By default, this command will also plot the True Positive, False Negative, Positive Predictive, and False Discovery rates in they grey-colored boxes. Please refer to the following example:
targetsVector = [1 2 1 1 3 2]; % True classes
outputsVector = [1 3 1 2 3 1]; % Predicted classes
% Convert this data to a [numClasses x 6] matrix
targets = zeros(3,6);
outputs = zeros(3,6);
targetsIdx = sub2ind(size(targets), targetsVector, 1:6);
outputsIdx = sub2ind(size(outputs), outputsVector, 1:6);
targets(targetsIdx) = 1;
outputs(outputsIdx) = 1;
% Plot the confusion matrix for a 3-class problem
plotconfusion(targets,outputs)
The class labels can be customized by setting that 'XTickLabel' and 'YTickLabel' properties of the axis:
h = gca;
h.XTickLabel = {'Class A','Class B','Class C',''};
h.YTickLabel = {'Class A','Class B','Class C',''};
h.YTickLabelRotation = 90;
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Michael Abboud il 6 Lug 2017
I have updated the above answer to better indicate that the 'TargetsVector' contains the true class labels.
I also included a quick example in the answer showing how to add strings as a name for each class, as I think that is a great easy way to make the plot more easily interpretable

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### Più risposte (1)

David Franco il 23 Gen 2018
Modificato: MathWorks Support Team il 16 Mar 2018
Implementation code:
Confusion Matrix
function [] = confusion_matrix(T,Y)
M = size(unique(T),2);
N = size(T,2);
targets = zeros(M,N);
outputs = zeros(M,N);
targetsIdx = sub2ind(size(targets), T, 1:N);
outputsIdx = sub2ind(size(outputs), Y, 1:N);
targets(targetsIdx) = 1;
outputs(outputsIdx) = 1;
% Plot the confusion matrix
plotconfusion(targets,outputs)
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