The following code is to classify the test data into several classes:function 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);Parzen classification error:0.35581How can I reduce the error coefficient of this code?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);
Classifying test data into several classes
ai;matlab;machine learning