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/chapters/chapter2/data_and_basic_modeling.html#sec-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$train()
• mlr3::LearnerClassif$predict_newdata_fast()

---

LearnerClassifGBM$new()

Creates a new instance of this R6 class.

Usage

LearnerClassifGBM$new()

---

LearnerClassifGBM$importance()

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

Usage

LearnerClassifGBM$importance()

Returns

Named numeric().

---

LearnerClassifGBM$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', predict_raw = 'FALSE'

# 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$model)
#> gbm::gbm(formula = f, data = data, keep.data = FALSE, n.cores = 1L)
#> A gradient boosted model with bernoulli loss function.
#> 100 iterations were performed.
#> There were 60 predictors of which 36 had non-zero influence.
print(learner$importance())
#>        V11         V4        V49        V10        V51        V45        V23 
#> 26.8225992 12.2122190  6.7750785  6.7667464  6.6971046  6.4556800  5.2868481 
#>        V36        V55        V48        V12        V59        V16        V35 
#>  4.8632794  4.6252629  4.3310776  3.6978704  3.6160188  3.5028517  3.4686759 
#>        V31        V52        V37        V43        V44         V6        V47 
#>  3.2512822  3.2139887  2.9024264  2.3962415  2.2983987  2.1899288  1.8405449 
#>        V33        V54        V27        V17        V29         V5        V19 
#>  1.7986845  1.7729890  1.3644644  1.0875491  0.8953636  0.8304425  0.6904209 
#>        V56        V21        V57         V9        V46         V8        V26 
#>  0.5066726  0.5020179  0.4793205  0.4361235  0.4312117  0.4050415  0.3399308 
#>        V28         V1        V13        V14        V15        V18         V2 
#>  0.3141052  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 
#>        V20        V22        V24        V25         V3        V30        V32 
#>  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 
#>        V34        V38        V39        V40        V41        V42        V50 
#>  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 
#>        V53        V58        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 
#>  0.2463768