fitcsvm cross-validation
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Hi, I am training a SVM classifier with the following code:
SVM_1=fitcsvm(X_train, y_train, 'OptimizeHyperparameters', 'all','HyperparameterOptimizationOptions',struct('Optimizer','bayesopt','AcquisitionFunctionName','expected-improvement-per-second-plus','Kfold',10,'ShowPlots',0));
I was wondering if there is any possibility to retrieve a performance metric of the classifier from the cross-validation - since I specify it as a 10-fold cross-validation (AUC, for example).
Thank you,
J
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Alan Weiss
2021-4-16
0 个投票
As shown in this doc example, the cross-validation loss is reported at the command line and plotted by default (I see that you turned off the plot). Is there something else that you need, or did I misunderstand you?
Alan Weiss
MATLAB mathematical toolbox documentation
3 个评论
João Mendes
2021-4-16
Alan Weiss
2021-4-16
The "Objective" in the iterative display (the generated table of iterations) is the cross-validation loss. The "Best so far" is simply the minimum objective up to that iteration. There is a difference between the "best so far" estimated and observed; that is a function of the model that the solver is estimating, and that changes every iteration. The model is that the observations themselves are noisy, so simply observing a value doesn't mean that observing it again will give the same response.
In a nutshell, I think that the iterative display gives you the information you seek.
Alan Weiss
MATLAB mathematical toolbox documentation
João Mendes
2021-4-16
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