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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 = 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