This function is intended to be given to a getImp
argument of Boruta
function to be called by the Boruta algorithm as an importance source.
This functionality is inspired by the Python package BoostARoota by Chase DeHan.
In practice, due to the eager way XgBoost works, this adapter changes Boruta into minimal optimal method, hence I strongly recommend against using this.
getImpXgboost(x, y, nrounds = 5, verbose = 0, ...)
data frame of predictors including shadows.
response vector.
Number of rounds; passed to the underlying xgboost
call.
Verbosity level of xgboost; either 0 (silent) or 1 (progress reports). Passed to the underlying xgboost
call.
other parameters passed to the underlying xgboost
call.
Similarly as nrounds
and verbose
, they are relayed from ...
of Boruta
.
For convenience, this function sets nrounds
to 5 and verbose to 0, but this can be overridden.