mlr_learners_classif.gamboost: Boosted Generalized Additive Classification Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Boosted Generalized Additive Classification Learner

Description

Fit a generalized additive classification model using a boosting algorithm. Calls mboost::gamboost() from mboost.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.gamboost")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, mlr3extralearners, mboost

Parameters

Id	Type	Default	Levels	Range
baselearner	character	bbs	bbs, bols, btree	-
dfbase	integer	4		(-\infty, \infty)
offset	numeric	NULL		(-\infty, \infty)
family	character	Binomial	Binomial, AdaExp, AUC, custom	-
custom.family	untyped	-		-
link	character	logit	logit, probit	-
type	character	adaboost	glm, adaboost	-
mstop	integer	100		(-\infty, \infty)
nu	numeric	0.1		(-\infty, \infty)
risk	character	inbag	inbag, oobag, none	-
oobweights	untyped	NULL		-
trace	logical	FALSE	TRUE, FALSE	-
stopintern	untyped	FALSE		-
na.action	untyped	stats::na.omit		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifGAMBoost

Methods

Public methods

• LearnerClassifGAMBoost$new()

• LearnerClassifGAMBoost$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

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Method new()

Create a LearnerClassifGAMBoost object.

Usage

LearnerClassifGAMBoost$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifGAMBoost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

be-marc

References

Bühlmann, Peter, Yu, Bin (2003). “Boosting with the L 2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324–339.

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.

Examples

# Define the Learner
learner = mlr3::lrn("classif.gamboost")
print(learner)

# 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)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

mlr-org/mlr3extralearners documentation built on Dec. 21, 2024, 2:21 p.m.