(1) Yes that's right. In that case it will optimize BoxConstraint and KernelScale.
(2) svmmod contains the SVM trained on the entire training data, using the best hyperparameters found. 5-fold crossvalidated misclassification rate was used as the objective function during optimization.
(3) You can optimize more variables. You can find out what hyperparameters are eligible like this:
>> h = hyperparameters('fitcsvm',ftTrn,CLTrn)
>> h.Name
h =
5×1 optimizableVariable array with properties:
Name
Range
Type
Transform
Optimize
ans =
'BoxConstraint'
ans =
'KernelScale'
ans =
'KernelFunction'
ans =
'PolynomialOrder'
ans =
'Standardize'
And then you can optimize additional hyperparameters like this:
svmmod=fitcsvm(ftTrn,CLTrn,'KernelFunction','rbf','OutlierFraction',0.05,...
'OptimizeHyperparameters',{'BoxConstraint','KernelScale','Standardize'},'HyperparameterOptimizationOptions',opts)
Because you're fixing the kernel function, the 'PolynomialOrder' hyperparameter is not relevant. So 'Standardize' ends up being the only additional hyperparameter.
One more note: Since you're now optimizing 3 variables, you might want to run the optimization longer, say 60 evaluations:
opts=struct('Optimizer','bayesopt','ShowPlots',true, 'Repartition',1, 'MaxObjectiveEvaluations',60);
svmmod=fitcsvm(ftTrn,CLTrn,'KernelFunction','rbf','OutlierFraction',0.05,...
'OptimizeHyperparameters',{'BoxConstraint','KernelScale','Standardize'},'HyperparameterOptimizationOptions',opts)
