Configuring Incremental XGBoost model
The "optimal" stopping point for XGBoost really depends on the data you feed into it.

Using Chunk N and Chunk N+1 for instance, consider the two scenarios:


data in Chunk N and Chunk N+1 is very different - it's likely
that the optimal stopping points for each chunk are very different.
data in Chunk N and Chunk N+1 is very similar - it's likely that the optimal stopping points for each chunk are quite similar.


I would suggest using a different approach - so for each chunk:


use the xgb.cv function to first determine the optimal stopping point
use the results from xgb.cv in xgb.train [i.e. set nrounds = xgb.cv$best.iteration]


As a side-note, have you explored any other techniques / tools to handle large data sets? In R, there are packages designed to tackle similar problems - e.g.bigmemory and ff.