clc
clear all
rawData = xlsread('new7.xlsx');
[m,n] = size(rawData);
new7 = rawData(randperm(m),:);
X_train=new7(1:1200,1:9);
y_train=new7(1:1200,10);
X_test=new7(1201:1999,1:9);
y_test=new7(1201:1999,10);
Mdl = fitcsvm(...
X_train, ...
y_train, ...
'KernelFunction','rbf',...
'OptimizeHyperparameters','auto',...
'HyperparameterOptimizationOptions',...
struct('AcquisitionFunctionName',...
'expected-improvement-plus'));
% Perform cross-validation
partitionedModel = crossval(Mdl, 'KFold', 10);
% Compute validation predictions
[validationPredictions, validationScores] = kfoldPredict(partitionedModel);
% Compute validation accuracy
validation_error = kfoldLoss(partitionedModel, 'LossFun', 'ClassifError'); % validation error
validationAccuracy = 1 - validation_error;
%% test model
oofLabel_n = predict(Mdl,X_test);
oofLabel_n = double(oofLabel_n); % chuyen tu categorical sang dang double
test_accuracy_for_iter = sum((oofLabel_n == (y_test)))/length(y_test)*100
%% save model
saveCompactModel(Mdl,'mySVM');
On observing your data set you will find that the labels for class 1 and 2 are clubbed together which leaves a discrepancy in the validation set and test set in which the validation set contained a majority of class 1 and some class 2 features while you test set had the opposite which resulted in poor accuracy, so before splitting the data into validation and test you should jumble the rows, so as to provide an equal distribution of classes across the two sets.
