How to change the default range of hyperparameters in fitcecoc function?

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Machine Learning Enthusiast
评论： Walter Roberson 2021-8-1
Is it possible to change the default paramater search range of fitcecoc function? I am trying to find the optimal paramters for SVM in custom range to reduce computational time. For example, I am trying to set below range for following parameters.
BoxConstraint = Positive values log-scaled in the range [1e-3,10]
KernelScale = Positive values log-scaled in the range [1e-3,10]
KernelFunction ='gaussian', , and 'polynomial'
PolynomialOrder=[2,5]
Any suggestions in this regard would be highly appreciated.
Demo Example:
clc
clear all
load fisheriris
t = templateSVM();
results = fitcecoc(meas, species,'Learners',t,'OptimizeHyperparameters','auto',...
'HyperparameterOptimizationOptions',struct('Optimizer',...
'randomsearch'))
T=results.HyperparameterOptimizationResults
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Machine Learning Enthusiast
Machine Learning Enthusiast 2021-7-26
Thank you. How to change other parameters? For instance, for kernal functiion, I am trying to search only guassian and polynomial in the search space.
Name: 'KernelFunction'
Range: {'gaussian' 'linear' 'polynomial'}
Type: 'categorical'
Transform: 'none'
Optimize: 0
Name: 'Coding'
Range: {'onevsall' 'onevsone'}
Type: 'categorical'
Transform: 'none'
Optimize: 1

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Walter Roberson
Walter Roberson 2021-7-26
In the section https://www.mathworks.com/help/stats/fitcecoc.html#d123e297982
Set nondefault parameters by passing a vector of optimizableVariable objects that have nondefault values. For example,
load fisheriris
params = hyperparameters('fitcecoc',meas,species,'svm');
params(2).Range = [1e-4,1e6];
After you created params look at
{params.Name}
to see which variable is which, to know which one to set the Range for.
  2 个评论
Machine Learning Enthusiast
Machine Learning Enthusiast 2021-7-26
Thank you. How to change other parameters? For instance, for kernal functiion, I am trying to search only guassian and polynomial in the search space.
Name: 'KernelFunction'
Range: {'gaussian' 'linear' 'polynomial'}
Type: 'categorical'
Transform: 'none'
Optimize: 0
Name: 'Coding'
Range: {'onevsall' 'onevsone'}
Type: 'categorical'
Transform: 'none'
Optimize: 1

请先登录，再进行评论。

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