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, 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$model)
#> gbm::gbm(formula = f, data = data, keep.data = FALSE, n.cores = 1L)
#> A gradient boosted model with bernoulli loss function.
#> 100 iterations were performed.
#> There were 60 predictors of which 35 had non-zero influence.
print(learner$importance())
#>        V11        V48        V37        V36        V51         V9        V49 
#> 21.1522990 12.0060745 11.3489343  8.8489546  7.7541323  6.0543380  5.9435030 
#>        V12        V52        V23        V43        V47        V45        V44 
#>  4.6846882  4.1797570  4.1509187  3.9204661  3.9146932  3.8058619  3.3294798 
#>         V4        V13        V22        V10        V29        V21        V59 
#>  3.1210972  2.7959860  2.0891096  2.0846778  2.0422818  1.9027149  1.7289538 
#>        V39        V15        V26         V6        V31        V18         V5 
#>  1.6572546  1.6028739  1.5165331  1.2911501  1.0812250  1.0448860  0.9919302 
#>        V32        V27        V30        V55         V1        V40        V53 
#>  0.9859648  0.8773184  0.7252717  0.7083302  0.6435555  0.3256014  0.2584024 
#>        V14        V16        V17        V19         V2        V20        V24 
#>  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 
#>        V25        V28         V3        V33        V34        V35        V38 
#>  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 
#>        V41        V42        V46        V50        V54        V56        V57 
#>  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 
#>        V58        V60         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 
#>  0.2318841