Python's XGBRegressor vs R's XGBoost
Since XGBoost uses decision trees under the hood it can give you slightly different results between fits if you do not fix random seed so the fitting procedure becomes deterministic.
You can do this via set.seed in R and numpy.random.seed in Python.
Noting Gregor's comment you might want to set nthread parameter to 1 to achieve full determinism.
I'm using python's XGBRegressor and R's xgb.train with the same parameters on the same dataset and I'm getting different predictions. 
I know that XGBRegressor uses 'gbtree' and I've made the appropriate comparison in R, however, I'm still getting different results. 
Can anyone lead me in the right direction on how to differentiate the 2 and/or find R's equivalence to python's XGBRegressor?
Sorry if this is a stupid question, thank you.