These are used to perform various service tasks either during boosting iterations or at the end. This approach helps to modularize many of such tasks without bloating the main training methods, and it offers .
By default, a callback function is run after each boosting iteration.
An R-attribute is_pre_iteration could be set for a callback to define a pre-iteration function.
When a callback function has finalize parameter, its finalizer part will also be run after
the boosting is completed.
WARNING: side-effects!!! Be aware that these callback functions access and modify things in the environment from which they are called from, which is a fairly uncommon thing to do in R.
To write a custom callback closure, make sure you first understand the main concepts about R environments.
Check either R documentation on environment or the
Environments chapter from the "Advanced R"
book by Hadley Wickham. Further, the best option is to read the code of some of the existing callbacks -
choose ones that do something similar to what you want to achieve. Also, you would need to get familiar
with the objects available inside of the xgb.train and xgb.cv internal environments.
cb.print.evaluation,
cb.evaluation.log,
cb.reset.parameters,
cb.early.stop,
cb.save.model,
cb.cv.predict,
xgb.train,
xgb.cv