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Gradient Boosting Classification Algorithm. Calls gbm::gbm() from gbm.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.gbm")

Meta Information

  • Task type: “classif”

  • Predict Types: “response”, “prob”

  • Feature Types: “integer”, “numeric”, “factor”, “ordered”

  • Required Packages: mlr3, mlr3extralearners, gbm

Parameters

IdTypeDefaultLevelsRange
distributioncharacterbernoullibernoulli, adaboost, huberized, multinomial-
n.treesinteger100\([1, \infty)\)
interaction.depthinteger1\([1, \infty)\)
n.minobsinnodeinteger10\([1, \infty)\)
shrinkagenumeric0.001\([0, \infty)\)
bag.fractionnumeric0.5\([0, 1]\)
train.fractionnumeric1\([0, 1]\)
cv.foldsinteger0\((-\infty, \infty)\)
keep.datalogicalFALSETRUE, FALSE-
verboselogicalFALSETRUE, FALSE-
n.coresinteger1\((-\infty, \infty)\)
var.monotoneuntyped--

Initial parameter values

  • keep.data is initialized to FALSE 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

Author

be-marc

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifGBM

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method importance()

The importance scores are extracted by gbm::relative.influence() from the model.

Usage

LearnerClassifGBM$importance()

Returns

Named numeric().


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifGBM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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