Gradient Boosting Classification Learner

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

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 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

• Dictionary of Learners: mlr3::mlr_learners.

• 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.

Author

be-marc

Super classes

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

Methods

Public methods

• LearnerClassifGBM$new()

• LearnerClassifGBM$importance()

• LearnerClassifGBM$clone()

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()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifGBM$new()

---

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, gbm
#> * Predict Types:  [response], prob
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: importance, missings, twoclass, weights

# 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$model)
#> gbm::gbm(formula = f, data = data, keep.data = FALSE, n.cores = 1L)
#> A gradient boosted model with bernoulli loss function.
#> 100 iterations were performed.
#> There were 60 predictors of which 36 had non-zero influence.
print(learner$importance())
#>        V12        V51        V31        V52        V49        V16        V37 
#> 12.9080150 10.8104200  8.8435043  8.7252821  8.5073837  7.6229224  7.5193383 
#>        V11         V9        V43        V55        V39        V21         V4 
#>  7.2799309  6.6637802  6.2327702  5.4502404  5.1442581  5.1277153  4.0201405 
#>        V48        V47        V59        V29        V15        V45        V10 
#>  3.1554837  3.1249549  3.0948468  2.8084338  2.1997178  1.8659082  1.8485970 
#>        V23        V32        V36        V28        V14         V8         V3 
#>  1.4264826  1.3365147  1.2698331  1.0885851  1.0344257  1.0203464  0.6233624 
#>        V22        V34         V1        V25        V44        V57         V5 
#>  0.5936044  0.5542828  0.5437610  0.5150026  0.5075722  0.4967042  0.4962864 
#>        V27        V13        V17        V18        V19         V2        V20 
#>  0.3096054  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 
#>        V24        V26        V30        V33        V35        V38        V40 
#>  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 
#>        V41        V42        V46        V50        V53        V54        V56 
#>  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 
#>        V58         V6        V60         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 
#>  0.2173913