Gradient Boosting Classification Learner
mlr_learners_classif.gbm.Rd
Gradient Boosting Classification Algorithm.
Calls gbm::gbm()
from gbm.
Meta Information
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, gbm
Parameters
Id | Type | Default | Levels | Range |
distribution | character | bernoulli | bernoulli, adaboost, huberized, multinomial | - |
n.trees | integer | 100 | \([1, \infty)\) | |
interaction.depth | integer | 1 | \([1, \infty)\) | |
n.minobsinnode | integer | 10 | \([1, \infty)\) | |
shrinkage | numeric | 0.001 | \([0, \infty)\) | |
bag.fraction | numeric | 0.5 | \([0, 1]\) | |
train.fraction | numeric | 1 | \([0, 1]\) | |
cv.folds | integer | 0 | \((-\infty, \infty)\) | |
keep.data | logical | FALSE | TRUE, FALSE | - |
verbose | logical | FALSE | TRUE, FALSE | - |
n.cores | integer | 1 | \((-\infty, \infty)\) | |
var.monotone | untyped | - | - |
Initial parameter values
keep.data
is initialized toFALSE
to save memory.n.cores
is initialized to 1 to avoid conflicts with parallelization through future.
References
Friedman, H J (2002). “Stochastic gradient boosting.” Computational statistics & data analysis, 38(4), 367–378.
See also
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Super classes
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifGBM
Methods
Method importance()
The importance scores are extracted by gbm::relative.influence()
from
the model.
Returns
Named numeric()
.
Examples
# Define the Learner
learner = mlr3::lrn("classif.gbm")
print(learner)
#> <LearnerClassifGBM:classif.gbm>: Gradient Boosting
#> * Model: -
#> * Parameters: keep.data=FALSE, n.cores=1
#> * Packages: mlr3, mlr3extralearners, gbm
#> * Predict Types: [response], prob
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: importance, missings, twoclass, weights
# Define a Task
task = mlr3::tsk("sonar")
# Create train and test set
ids = mlr3::partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
#> Distribution not specified, assuming bernoulli ...
print(learner$model)
#> gbm::gbm(formula = f, data = data, keep.data = FALSE, n.cores = 1L)
#> A gradient boosted model with bernoulli loss function.
#> 100 iterations were performed.
#> There were 60 predictors of which 42 had non-zero influence.
print(learner$importance())
#> V11 V9 V12 V44 V13 V31 V51
#> 12.6008956 11.7525462 11.6497257 7.0666897 6.7942342 6.1490541 6.0363449
#> V22 V52 V36 V49 V48 V40 V39
#> 4.5755311 4.5120352 4.4656788 4.4634687 4.1842278 3.9818023 3.2945606
#> V37 V27 V10 V54 V32 V4 V45
#> 3.2614260 3.1027780 2.9733663 2.7780510 2.6653824 2.4417863 2.3918495
#> V16 V60 V26 V43 V46 V47 V55
#> 2.3411173 2.2633236 1.5173297 1.2631283 1.2240094 1.1395545 1.1339056
#> V53 V15 V1 V21 V59 V42 V17
#> 1.0637253 1.0300141 0.9857307 0.9020723 0.6087593 0.5901937 0.5892742
#> V23 V20 V56 V8 V38 V50 V19
#> 0.5739356 0.5071219 0.4772756 0.4739872 0.4502871 0.4286625 0.4138723
#> V14 V18 V2 V24 V25 V28 V29
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V3 V30 V33 V34 V35 V41 V5
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V57 V58 V6 V7
#> 0.0000000 0.0000000 0.0000000 0.0000000
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.173913