I am going to submit the code below for a confusion matrix that I have made. (Okay, with some help from another.) When I run the code with my deep learning program, it seems that I get more misses than hits. In other words, I have a lot of entries for "U," which is an unknown variable, a "miss" in other words. I have 154 data files in which the computer is to learn and distinguish between Object A and Object B. What am I doing wrong? I do not think that the data is incorrect or needs to be massaged more. I think the error is in the confusion matrix that I have made. Help! I am confused about the confusion matrix. Can you have a look at the code and possibly help? Here is the code below. Many thanks!
%% Section 12: Create Confusion Matrix Chart
% From the MathWorks Help Center web-site under "confusionchart."
% Description
% confusionchart(trueLabels,predictedLabels) creates a confusion matrix chart from true labels trueLabels and
% predicted labels predictedLabels and returns a ConfusionMatrixChart object.
% The rows of the confusion matrix correspond to the true class and the columns correspond to the predicted class.
% Diagonal and off-diagonal cells correspond to correctly and incorrectly classified observations, respectively.
% Use cm to modify the confusion matrix chart after it is created.
% For a list of properties, see ConfusionMatrixChart Properties.
% Create Confusion Matrix Chart.
% Load a sample of predicted and true labels for a classification problem.
% trueLabels is the true labels for an image classification problem and
% predictedLabels is the predictions of a convolutional neural network.
trueLabels = dsTest.UnderlyingDatastores{1,2}.LabelData(:,2);
newTrueLabels = {};
for idx = 1:numel(trueLabels)
if trueLabels{idx} == 'Van'
newTrueLabels{idx} = 'V';
elseif trueLabels{idx} == 'Man'
newTrueLabels{idx} = 'M';
else
newTrueLabels{idx} = 'U'; % U means 'Unknown'. That is, it is a miss and neither 'Van' nor 'Man'.
end
end
predictedLabels = results.Labels;
newPredictedLabels = {};
for idx = 1:numel(predictedLabels)
if predictedLabels{idx} == 'Van'
newPredictedLabels{idx} = 'V';
elseif predictedLabels{idx} == 'Man'
newPredictedLabels{idx} = 'M';
else
newPredictedLabels{idx} = 'U'; % U means 'Unknown'. That is, it is a miss and neither 'Van' nor 'Man'.
end
end
% Create a confusion matrix chart.
figure(1)
cm = confusionchart(newTrueLabels,newPredictedLabels);
% Modify the appearance and behavior of the confusion matrix chart by changing property values.
% Add column and row summaries and a title.
% A column-normalized column summary displays the number of correctly and incorrectly classified observations
% for each predicted class as percentages of the number of observations of the corresponding predicted class.
% A row-normalized row summary displays the number of correctly and incorrectly classified observations
% for each true class as percentages of the number of observations of the corresponding true class.
cm.ColumnSummary = 'column-normalized';
cm.RowSummary = 'row-normalized';
cm.Title = 'Confusion Matrix for Man and Van Data';
disp("End of Section 12");
disp(" ");
