How to access each classifier's predicted class and accuracy from the 'fitcensemble' function generated model?

For some context, I am using a homogenous ensemble with the bagging method. I am trying to modify the default voting system used by MATLAB while using fitcensemble as it is not clear how the function does this by default.

Any information on the following would be much appreciated:

• How does MATLAB combine votes by default in ensemble classification?
• Can this be changed programatically to use a different (or custom) method of voting?
• Can each classifier's predicted class and accuracy from the model be obtained to make my own voting fuction?

Thank you

Answer 1

[Ayush Aniket]

In ensemble methods like bagging, MATLAB combines votes using majority voting (averaging) for classification. Each classifier in the ensemble votes for a class, and the class with the most votes is selected as the final prediction. Refer the following documentation to read about the algorithm:

https://www.mathworks.com/help/stats/ensemble-algorithms.html#bsw8at7

There is no direct support for changing the voting mechanism within fitcensemble function. However, you can implement a custom voting mechanism by accessing individual classifier predictions as shown below:

% Train an ensemble model using bagging
ensModel = fitcensemble(X, Y, 'Method', 'Bag');

% Access individual learners
numLearners = ensModel.NumTrained;
individualPredictions = zeros(numSamples, numLearners);

for i = 1:numLearners
    % Get predictions from each individual learner
    individualPredictions(:, i) = predict(ensModel.Trained{i}, X);
end

%Implement custom logic