1) I would like to suggest adding the option to bootstrap to the validation process in the platforms. Either as a validation option directly (i.e. as one more alternative to the existing K-fold, holdout, etc), or as a nested option i.e.bootstrap the holdout or K-folds. In my mind it is more intuitive and makes sense from a workflow perspective. Also, if one bootstraps at the end by right-clicking results, one gets the bootstrap only of a specific table and not the whole model's estimates i.e. if I bootstrap a table, I get the table results but not the results from the residuals or diagnostics etc. Moreover, the resulting table is raw and unprocessed and lacks context; it is also more involved to use those results to assess and improve the model. If I bootstrap a model I would then need to take those results and further work with them to integrate the results into my analytical process and decision making. Having the bootstrap integrated into the modeling platform would include the bootstrapped estimates and results for the whole model as separate table results. It would leverage the bootstrapping option to really use it for improving modeling (better models, more analytics on their performance and stability, etc).
2) Related to that is adding the option to bootstrap every model fit in the platform. For example logistic fits don't have validation, and using the bootstrap with the output table is very onerous to the point of being useless for a user that doesn't know scripting and stats theory to high level. Adding the option to bootstrap to the platform would make it much easier to manage. Nowadays doing validation is standard for almost any fit where there is a significant number of predictors, regardless of the fit platform used.
Uriel.