getImpXgboost {Boruta}R Documentation

Xgboost importance

Description

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.

Usage

getImpXgboost(x, y, nrounds = 5, verbose = 0, ...)

Arguments

x

data frame of predictors including shadows.

y

response vector.

nrounds

Number of rounds; passed to the underlying xgboost call.

verbose

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.

Note

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.

References

https://github.com/chasedehan/BoostARoota


[Package Boruta version 6.0.0 Index]