Plot classification confusion matrix
plotconfusion(targets,outputs) takes target
and output data and generates a confusion plot. The target data are
ground truth labels in 1-of-N form (in each column, a single element
is 1 to indicate the correct class, and all other elements are 0).
The output data are the outputs from a neural network that performs
classification. They can either be in 1-of-N form, or may also be
probabilities where each column sums to 1.
several confusion plots in one figure, and prefixes the character
strings specified by the
'name' arguments to the
titles of the appropriate plots.
On the confusion matrix plot, the rows show the predicted class, and the columns show the true class. The diagonal cells show where the true class and predicted class match. The off diagonal cells show instances where the classifier has made mistakes. The column on the right hand side of the plot shows the accuracy for each predicted class, while the row at the bottom of the plot shows the accuracy for each true class. The cell in the bottom right of the plot shows the overall accuracy.
This example shows how to train a pattern recognition network and plot its accuracy.
[x,t] = simpleclass_dataset; net = patternnet(10); net = train(net,x,t); y = net(x); plotconfusion(t,y)