mlr_learners_regr.gbm.Rd
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("regr.gbm") lrn("regr.gbm")
Packages: gbm
Predict Types: response
Feature Types: integer, numeric, factor, ordered
Properties: importance, missings, weights
keep_data
:
Actual default: TRUE
Adjusted default: FALSE
Reason for change: keep_data = FALSE
saves memory during model fitting.
n.cores
:
Actual default: NULL
Adjusted default: 1
Reason for change: Suppressing the automatic internal parallelization if
cv.folds
> 0.
be-marc
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrGBM
new()
Creates a new instance of this R6 class.
LearnerRegrGBM$new()
importance()
The importance scores are extracted by gbm::relative.influence()
from
the model.
LearnerRegrGBM$importance()
Named numeric()
.
clone()
The objects of this class are cloneable with this method.
LearnerRegrGBM$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("regr.gbm")) print(learner)#> <LearnerRegrGBM:regr.gbm> #> * Model: - #> * Parameters: keep.data=FALSE, n.cores=1 #> * Packages: gbm #> * Predict Type: response #> * Feature types: integer, numeric, factor, ordered #> * Properties: importance, missings, weights# available parameters: learner$param_set$ids()#> [1] "distribution" "n.trees" "interaction.depth" #> [4] "n.minobsinnode" "shrinkage" "bag.fraction" #> [7] "train.fraction" "cv.folds" "alpha" #> [10] "keep.data" "verbose" "n.cores" #> [13] "var.monotone"