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())
#> V12 V51 V37 V11 V27 V49 V23
#> 21.7932817 17.8604108 7.6195480 7.2659995 7.0067308 6.5206732 4.3453791
#> V31 V9 V52 V45 V48 V20 V6
#> 4.2627508 4.2153837 4.1784812 3.5621674 3.2407680 3.0740564 3.0672750
#> V16 V46 V21 V26 V28 V60 V44
#> 2.8626640 2.7905101 2.4422467 2.4002990 2.3649070 2.2363486 2.0542506
#> V43 V59 V36 V54 V17 V15 V30
#> 1.9678551 1.7731430 1.7674888 1.6405728 1.5080317 1.4663449 1.1391682
#> V29 V32 V53 V55 V56 V41 V22
#> 1.0477439 0.9412785 0.8866315 0.7104812 0.6433514 0.6097913 0.5761004
#> V24 V40 V1 V10 V13 V14 V18
#> 0.4049575 0.3878782 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V19 V2 V25 V3 V33 V34 V35
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V38 V39 V4 V42 V47 V5 V50
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V57 V58 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
#> 0.1884058