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At the moment, mean and sd of each performance metric is returned based on its values across cross-validation folds. It would be useful for meta-analysis to be able to extract the individual performance metrics for each individual fold.
For the tune_gbm function this would mean filtering eval.partial for the best fit and adding it to the results of the function when the user specifies an option to hold on to this info, similar to the keep.fold.fit in the gbm.step function of the dismo package
The text was updated successfully, but these errors were encountered:
At the moment, mean and sd of each performance metric is returned based on its values across cross-validation folds. It would be useful for meta-analysis to be able to extract the individual performance metrics for each individual fold.
For the
tune_gbm
function this would mean filteringeval.partial
for the best fit and adding it to the results of the function when the user specifies an option to hold on to this info, similar to thekeep.fold.fit
in thegbm.step
function of thedismo
packageThe text was updated successfully, but these errors were encountered: