objective function in Bayesian Optimization Algorithm like fitrsvm and fitrgp

Question

Dimitri 2019-5-13

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/461697-objective-function-in-bayesian-optimization-algorithm-like-fitrsvm-and-fitrgp

评论： antlhem 2021-5-29

Hello,

What is the mathematical objective function in the bayesian optimization algorithm? The explanation says that the algorithm like fitrsvm tries to minimize the log(1 + cross-validation loss) but what is the real mathematical formula?

Is it possible to change the objective function to just the MSE?

Thank you!

Dimitri

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Don Mathis 2019-5-13

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/461697-objective-function-in-bayesian-optimization-algorithm-like-fitrsvm-and-fitrgp#answer_374750

This page says that the loss defaults to MSE. So that's the loss that's used in the log(1+cvloss) formula. Cross validated loss is the loss summed over all the held-out validation sets. The default when using optimization is 5-fold cross-validation.

There's not an option to change the hyperparameter optimization objective function from log(1+cvloss). You would need to edit the source code to do that. The source file is matlab\toolbox\stats\classreg\+classreg\+learning\+paramoptim\createObjFcn.m. Look for the call to the log1p function.

3 个评论
显示 1更早的评论隐藏 1更早的评论

Don Mathis 2019-5-14

编辑：Don Mathis 2019-5-14

Because loss(Mdl,X,Y) is the loss of the final model on the full dataset, while the MinObjective is the log of 1 plus the out-of-sample cross-validated loss. See the kfoldLoss method for documentation of that. If you used 5-fold cross-validation, the kfoldLoss is the summed loss of 5 different models, each on 1/5 of the dataset. It is not the loss of the final model on the full dataset.

antlhem 2021-5-29

Hi, Could take a look into my question? https://uk.mathworks.com/matlabcentral/answers/842800-why-matlab-svr-is-not-working-for-exponential-data-and-works-well-with-data-that-fluctuates?s_tid=prof_contriblnk

请先登录，再进行评论。