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How to show the accuracy on a confusion matrix

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Nick Vasilakis
Nick Vasilakis 2022-4-17
回答： Chunru 2022-4-18
Hello!
So I've trained a perceptron with 29 data and I implemented its confusion matrix. Yet I want it to show me the accuracy. So far it only appears as follows:
And this is the code that I used to plot it:
function katataksh= Ask31c (w1,w2,b,x_new,y_new)
W=[w1 w2 b]; %ορίζω τον πίνακα W να παίρνει τις τιμές των w1,w2 και b
New_data=x_new;
y_problepsi_new=sign(New_data(:,1)*W(1,1)+New_data(:,2)*W(1,2)+W(1,3));
katataksh=y_problepsi_new;
apodosh=confusionmat(y_new,katataksh);
accuracy=sum(y_new == katataksh,'all')/numel(katataksh);
confusionchart(apodosh);
end
Note that there's more code that is implemented, but I think it's not necessary to write it here, since I only want to find the accuracy of the confusion matrix.
Thank you in advance!

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回答（1 个）

Chunru
Chunru 2022-4-18
Ran in:
You nee to add column and row summaries. Check out the following example from doc.
load fisheriris
X = meas;
Y = species;
Mdl = fitcknn(X,Y,'NumNeighbors',5,'Standardize',1);
predictedY = resubPredict(Mdl);
cm = confusionchart(Y,predictedY);
% The confusion matrix displays the total number of observations in each cell.
% 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.
%
% By default, confusionchart sorts the classes into their natural order as defined
% by sort. In this example, the class labels are character vectors, so confusionchart
% sorts the classes alphabetically. Use sortClasses to sort the classes by a specified
% order or by the confusion matrix values.
%
% The NormalizedValues property contains the values of the confusion matrix. Display
% these values using dot notation.
cm.NormalizedValues
ans = 3×3
50 0 0 0 47 3 0 4 46
% Modify the appearance and behavior of the confusion matrix chart by changing property
% values. Add a title.
cm.Title = 'Iris Flower Classification Using KNN';
% Add column and row summaries.
cm.RowSummary = 'row-normalized';
cm.ColumnSummary = 'column-normalized';

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