Gradient Boosting Classification Learner
Source:R/learner_gbm_classif_gbm.R
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
Inherited methods
mlr3::Learner$base_learner()
mlr3::Learner$configure()
mlr3::Learner$encapsulate()
mlr3::Learner$format()
mlr3::Learner$help()
mlr3::Learner$predict()
mlr3::Learner$predict_newdata()
mlr3::Learner$print()
mlr3::Learner$reset()
mlr3::Learner$selected_features()
mlr3::Learner$train()
mlr3::LearnerClassif$predict_newdata_fast()
Method importance()
The importance scores are extracted by gbm::relative.influence()
from
the model.
Returns
Named numeric()
.
Examples
# Define the Learner
learner = 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 = tsk("sonar")
# Create train and test set
ids = partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
#> Distribution not specified, assuming bernoulli ...
print(learner$model)
#> gbm::gbm(formula = f, data = data, keep.data = FALSE, n.cores = 1L)
#> A gradient boosted model with bernoulli loss function.
#> 100 iterations were performed.
#> There were 60 predictors of which 38 had non-zero influence.
print(learner$importance())
#> V11 V9 V52 V4 V44 V37 V10
#> 20.3694228 17.0862776 10.1182312 9.6102914 7.6197214 5.8258659 5.5397393
#> V17 V43 V23 V12 V49 V55 V46
#> 5.0484641 4.9268203 4.1030514 3.9421533 3.4890607 3.4192754 3.2273799
#> V16 V51 V27 V36 V5 V50 V59
#> 3.0780890 2.8593813 2.8039052 2.6360845 2.5318223 1.8068143 1.4316266
#> V48 V21 V31 V60 V57 V26 V1
#> 1.4258798 1.3179666 1.2952165 1.0370650 1.0161219 0.6347199 0.6234034
#> V42 V33 V38 V28 V54 V15 V56
#> 0.6004960 0.5779516 0.5632938 0.5267435 0.5000487 0.4921677 0.4815109
#> V13 V45 V8 V14 V18 V19 V2
#> 0.4179504 0.3941090 0.3703912 0.0000000 0.0000000 0.0000000 0.0000000
#> V20 V22 V24 V25 V29 V3 V30
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V32 V34 V35 V39 V40 V41 V47
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V53 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
#> 0.2028986