Why almost the same optimization function gives different results?

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Nadou
Nadou 2021-7-16
评论： Nadou 2021-7-19
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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回答（1 个）

Alan Weiss
Alan Weiss 2021-7-16
编辑：Alan Weiss 2021-7-18
I am not 100% sure, but my reading of the fitcecoc documentation shows that 'auto' has this description:
'auto' — Use {'Coding'} along with the default parameters for the specified Learners:
  • Learners = 'svm' (default) — {'BoxConstraint','KernelScale'}
So I think that 'auto' is equivalent to {'Coding','BoxConstraint','KernelScale'}.
Alan Weiss
MATLAB mathematical toolbox documentation
  1 个评论
Nadou
Nadou 2021-7-19
Hello Alan,
Thank you for your response
This is what I thought also while reading fitcecoc documentation. However, I found different results
Best regards

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