What do h2o
checkpoints actually do? Does a model created with say
gbm_continued = H2OGradientBoostingEstimator(checkpoint= gbm_orig.model_id, ntrees = 50, seed = 1234)
mean that gbm_continued will have the same parameters and prediction performance as gbm_orig if we were to not train it on any new data?
The docs, say "This will build a new model as a continuation of a previously generated model", but I am confused as to what a "continuation" actually implies. An explanation would be much appreciated. Thanks