I have trained a ML model in regression learner app using optimizable GPR model using the default setting such as 5 k-fold validation and 30 iterations etc. Now I am traying to do the same using the Matlab script.using the following where X are the resgressors and Y is the response variable.
>> ML_mdl=fitrgp(X,Y,’OptimizeHyperparameters’,’all’,’HyperparameterOptimizationOptions’,struct(‘KFold’,5))
Are the two resulting models more or less equivalent? I know there will be some difference due to the probabilistic nature of the algorithm. When I test it on the entire training set, the R squared value is practically 1.0. Is it overfitting even with K-fold cross-correlation? The prediction on unseen testing set is not that good. Any suggestions?I have trained a ML model in regression learner app using optimizable GPR model using the default setting such as 5 k-fold validation and 30 iterations etc. Now I am traying to do the same using the Matlab script.using the following where X are the resgressors and Y is the response variable.
>> ML_mdl=fitrgp(X,Y,’OptimizeHyperparameters’,’all’,’HyperparameterOptimizationOptions’,struct(‘KFold’,5))
Are the two resulting models more or less equivalent? I know there will be some difference due to the probabilistic nature of the algorithm. When I test it on the entire training set, the R squared value is practically 1.0. Is it overfitting even with K-fold cross-correlation? The prediction on unseen testing set is not that good. Any suggestions? I have trained a ML model in regression learner app using optimizable GPR model using the default setting such as 5 k-fold validation and 30 iterations etc. Now I am traying to do the same using the Matlab script.using the following where X are the resgressors and Y is the response variable.
>> ML_mdl=fitrgp(X,Y,’OptimizeHyperparameters’,’all’,’HyperparameterOptimizationOptions’,struct(‘KFold’,5))
Are the two resulting models more or less equivalent? I know there will be some difference due to the probabilistic nature of the algorithm. When I test it on the entire training set, the R squared value is practically 1.0. Is it overfitting even with K-fold cross-correlation? The prediction on unseen testing set is not that good. Any suggestions? regression learner, script based ml model training MATLAB Answers — New Questions

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