Below I have code, in which I am trying to train an XGBoost model in R that early stops after a given number of rounds early_stopping_rounds without improvement.
watchlist <- list(train=dtrain, test=dtest)
param <- list(
objective = "binary:logistic",
eta = 0.3,
max_depth = 8,
eval_metric="logloss"
)
xgb.train(params = param, data = dtrain, nrounds = 1000, watchlist = watchlist, early_stopping_rounds = 3)
However, instead of fixing the number of rounds, I would like to pass a min_delta value, so the early stopping kicks in when the difference between rounds is below a given tolerance.
Others (here and here) have asked this for Python. However, advances not too long ago have implemented this option for Python.
But how do I work this out in R? Is there something like it?
