XGboost classification with very small data set
You can use grid search or xgb.cv for find the best iteration. Run xgb.cv for example 500 trees and add early stopping criteria. Then you can use the best iteration for xgb.train. Have a look at these links.

Hypertuning XGBoost parameters

https://stats.stackexchange.com/questions/171043/how-to-tune-hyperparameters-of-xgboost-trees