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Bayesian Optimization <undefined> and NaN Results

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MByk
MByk on 26 Jun 2018
Edited: MByk on 27 Jun 2018
Sorry, if it's a silly question. I am using Bayesian Optimization to optimize classifier hyperparameters but sometimes I having "<undefined>" and "NaN" values for some parameters. What do they mean? Dataset is not suitable for this classifier? Should I use classifier's default parameters? Thanks for the help.

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Don Mathis
Don Mathis on 26 Jun 2018
Edited: Don Mathis on 26 Jun 2018
I would need to see your example to be sure, but a typical case is when some parameter is not used when some other parameter has a certain value. For example, the PolynomialOrder parameter of an SVM is only used when the KernelFunction parameter is 'polynomial'. So a NaN or "<undefined>" value in a parameter vector means that you should not use that parameter.

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MByk
MByk on 26 Jun 2018
Thank you very much.
X = DataSet(:,(1:end-1));
Y = DataSet(:,end);
Disp_Opts = struct('Optimizer','bayesopt','ShowPlots',false,...
'Verbose',1,'AcquisitionFunctionName','expected-improvement-plus');
Mdl_Eva = fitcnb(X,Y,'OptimizeHyperparameters','all',...
'HyperparameterOptimizationOptions',Disp_Opts);

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