mlr_learners_classif.glmboost | R Documentation |
Fit a generalized linear classification model using a boosting algorithm.
Calls mboost::glmboost()
from mboost.
This Learner can be instantiated via lrn():
lrn("classif.glmboost")
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, mboost
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 | - | - | |
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifGLMBoost
new()
Create a LearnerClassifGLMBoost
object.
LearnerClassifGLMBoost$new()
clone()
The objects of this class are cloneable with this method.
LearnerClassifGLMBoost$clone(deep = FALSE)
deep
Whether to make a deep clone.
be-marc
Bühlmann, Peter, Yu, Bin (2003). “Boosting with the L 2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324–339.
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.
# 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()
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