Suppose, I have a data set of different types of cards with eight features in my hand. I want to find features to predict the classes of the cards (diamonds=1, hearts=2, clubs=3, and, spades=4).------------------------------------------------------------------------------|   f1   |   f2   |   f3    |   f4    |    f5    |   f6    |   f7    |  f8+--------+--------+---------+---------+---------------------------------------|        |        |         |         |          |         |         |f1 column is for class-labels and the rest of them are features.I want to classify the cards using two times higher apriori probability only for red suits.How can I do this? Can I do this without modifying my bayes() function and parzen() function?Source Code and resources:Training data: train.txtTest data: test.txtDriver Programfunction errcf = bayescls(train, test, hpdf, apriori, winwidth)% Bayes classifier% train - training set; the first column contains label% test - test set; the first column contains label% hpdf - handle to function used to compute probability density% apriori - row vector of a priori probabilities for all classes% winwidth - window width (just for Parzen window hpdf function)  clpdf = hpdf(train, test(:,2:end), winwidth);  clpr = clpdf .* repmat(apriori, rows(test), 1);  [val lab] = max(clpr, [], 2);  errcf = mean(test(:,1) ~= lab);PDF calculation using Parzen Windowfunction pdfmx = pdfparzen(train, test, winwidth)% computes probability density for all classes% using Parzen window approximation% train - train set; the first column contains label% used to  compute mean and variation for all classes% test - test set (without labels)% winwidth - width of the Parzen window % pdfmx - matrix of probability density for all classes%   class with label idx is stored in pdfmx(:,idx)classnb = rows(unique(train(:,1)));pdfmx = ones(rows(test), classnb);for samp=1:rows(test)for cl=1:classnbclidx = train(:,1) == cl;indiv = zeros(sum(train(:,1) == cl), columns(test));for feat=1:columns(test)indiv(:,feat) = normpdf(test(samp,feat), train(clidx, feat + 1), winwidth);endpdfmx(samp,cl) = mean(prod(indiv,2));endendMatlab command line usage>> ercf_parzen = bayescls(train, test, @pdfparzen, 0.25 * ones(1,4), 0.1);ercf_parzen = 0.35581
Classify features using two times higher apriori probability only for specific class(es)
classification;probability;naive bayes classifier