getImpXgboost {Boruta} | R Documentation |
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.
getImpXgboost(x, y, nrounds = 5, verbose = 0, ...)
x |
data frame of predictors including shadows. |
y |
response vector. |
nrounds |
Number of rounds; passed to the underlying |
verbose |
Verbosity level of xgboost; either 0 (silent) or 1 (progress reports). Passed to the underlying |
... |
other parameters passed to the underlying |
Only dense matrix interface is supported; all predictions given to Boruta
call have to be numeric (not integer).
Categorical features should be split into indicator attributes.
This functionality is inspired by the Python package BoostARoota by Chase DeHan.
I have some doubts whether boosting importance can be used for all relevant selection without hitting substantial false negative rates; please consider this functionality experimental.
https://github.com/chasedehan/BoostARoota