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, and gbm
#> • Predict Types: [response] and prob
#> • Feature Types: integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: importance, missings, twoclass, and weights
#> • Other settings: use_weights = 'use'
# 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$model)
#> gbm::gbm(formula = f, data = data, keep.data = FALSE, n.cores = 1L)
#> A gradient boosted model with bernoulli loss function.
#> 100 iterations were performed.
#> There were 60 predictors of which 37 had non-zero influence.
print(learner$importance())
#> V11 V49 V37 V12 V17 V52 V1
#> 16.8334158 8.9788930 8.4600096 8.4590155 6.7847252 6.0076082 5.8083242
#> V36 V21 V31 V48 V47 V27 V51
#> 5.6596724 4.8891243 4.6876254 4.5317000 4.2735687 4.2628761 4.0644592
#> V15 V23 V44 V45 V4 V10 V35
#> 3.7149247 3.4414170 3.1397047 3.0385145 2.8284521 2.4879346 2.4406942
#> V54 V53 V28 V50 V42 V55 V60
#> 2.3980269 2.0641484 1.6563650 1.3591160 1.1505055 1.1370327 0.8238090
#> V33 V22 V20 V19 V30 V40 V9
#> 0.7863335 0.7850850 0.7067588 0.7025799 0.6257914 0.5429188 0.4463202
#> V16 V43 V13 V14 V18 V2 V24
#> 0.3786735 0.3652146 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V25 V26 V29 V3 V32 V34 V38
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V39 V41 V46 V5 V56 V57 V58
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V59 V6 V7 V8
#> 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
#> 0.2173913