Hi Tintumon,
I gather that in the first query, you'd like to find out how to implement Stratified k-fold cross validation. Then, you'd like to find out how to make a distance calculator by yourself.
Now, for the first part, I could not find anything that automatically startifies the dataset in the 'crossval' function's arguments. On the other hand, you can give it a 'cvpartition' object, which is basically how the data is split. You can refer this for more about the 'Partition' argument in 'crossval' here: https://www.mathworks.com/help/stats/crossval.html#mw_e1a8945e-7fad-4fa9-93cb-518884197429
You might want to use the 'cvpartition' function, to get the above object. While doing this, you can set the 'Stratify' flag to true. More information is here: https://www.mathworks.com/help/stats/cvpartition.html#mw_319d54b6-08be-4b1d-b444-97ab793fb03c
The updated code might look something like this:
cvp = cvpartition(response, 'KFold', 10, 'Stratify', true);
partitionedModel = crossval(trainedClassifier.ClassificationKNN, 'Partition', cvp);
Coming to the next query, you can define the custom distance function as a matlab function, and then update the code in the following way to use it as the distance function. Please make sure that the inputs and outputs are consistent with those in the MathWorks given distance functions.
classificationKNN = fitcknn(...
predictors, ...
response, ...
'Distance', @customDistance, ... % Use your custom distance function
'NumNeighbors', 19, ...
'DistanceWeight', 'SquaredInverse', ...
'Standardize', false, ...
'ClassNames', [0; 1; 2]);
function dists = customDistance(X1, X2)
% Replace this with your actual Bhattacharyya distance calculation
end
Hope this helps!
