- Learners = 'svm' (default) — {'BoxConstraint','KernelScale'}
Why almost the same optimization function gives different results?
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Hello,
I am trying to optimize ECOC classifier as follows:
%data
clear all
load fisheriris
X = meas;Y = species;
rng default
t_gaussian=templateSVM('KernelFunction','gaussian','standardize',true)
Mdl_gaussian = fitcecoc(X,Y,'Coding','onevsall','Learners',t_gaussian,'OptimizeHyperparameters','auto',...
'HyperparameterOptimizationOptions',struct('CVPartition',CVO,'Optimizer','bayesopt','AcquisitionFunctionName',...
'expected-improvement-plus'))
I am wondering why I did not find the same results if I remplace 'OptimizeHyperparameters','auto' with 'OptimizeHyperparameters',{'BoxConstraint','KernelScale'}
rng default
Mdl_g = fitcecoc(X,Y,'Coding','onevsall','Learners',t_gaussian,'OptimizeHyperparameters',{'BoxConstraint','KernelScale'},...
'HyperparameterOptimizationOptions',struct('CVPartition',CVO,'Optimizer','bayesopt','AcquisitionFunctionName',...
'expected-improvement-plus'))
Best regards
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Alan Weiss
le 16 Juil 2021
Modifié(e) : Alan Weiss
le 18 Juil 2021
I am not 100% sure, but my reading of the fitcecoc documentation shows that 'auto' has this description:
So I think that 'auto' is equivalent to {'Coding','BoxConstraint','KernelScale'}.
Alan Weiss
MATLAB mathematical toolbox documentation
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