mlr_learners_classif.glmboost: Boosted Generalized Linear Classification Learner

mlr_learners_classif.glmboostR Documentation

Boosted Generalized Linear Classification Learner

Description

Fit a generalized linear classification model using a boosting algorithm. Calls mboost::glmboost() from mboost.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.glmboost")

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
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 -
center logical TRUE TRUE, FALSE -
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 -
contrasts.arg untyped - -

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifGLMBoost

Methods

Public methods

Inherited methods

Method new()

Create a LearnerClassifGLMBoost object.

Usage
LearnerClassifGLMBoost$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClassifGLMBoost$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

Examples


# Define the Learner
learner = mlr3::lrn("classif.glmboost")
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 Nov. 11, 2024, 11:11 a.m.