Boosted Generalized Linear Regression Learner

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

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.glmboost")

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

• Required Packages: mlr3, mlr3extralearners, mboost

Parameters

Id	Type	Default	Levels	Range
offset	numeric	NULL		\((-\infty, \infty)\)
family	character	Gaussian	Gaussian, Laplace, Huber, Poisson, GammaReg, NBinomial, Hurdle, custom	-
custom.family	untyped	-		-
nuirange	untyped	c(0, 100)		-
d	numeric	NULL		\((-\infty, \infty)\)
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	-		-

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.

Author

be-marc

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrGLMBoost

Methods

Public methods

• LearnerRegrGLMBoost$new()

• LearnerRegrGLMBoost$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()

---

Method new()

Create a LearnerRegrGLMBoost object.

Usage

LearnerRegrGLMBoost$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrGLMBoost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("regr.glmboost")
print(learner)
#> <LearnerRegrGLMBoost:regr.glmboost>: Boosted Generalized Linear Model
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, mboost
#> * Predict Types:  [response]
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: weights

# Define a Task
task = mlr3::tsk("mtcars")

# 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)
#> 
#> 	 Generalized Linear Models Fitted via Gradient Boosting
#> 
#> Call:
#> glmboost.formula(formula = f, data = data, family = new("boost_family_glm",     fW = function (f)     return(rep(1, length = length(f))), ngradient = function (y,         f, w = 1)     y - f, risk = function (y, f, w = 1)     sum(w * loss(y, f), na.rm = TRUE), offset = function (x,         w, ...)     UseMethod("weighted.mean"), check_y = function (y)     {        if (!is.numeric(y) || !is.null(dim(y)))             stop("response is not a numeric vector but ", sQuote("family = Gaussian()"))        y    }, weights = function (w)     {        switch(weights, any = TRUE, none = isTRUE(all.equal(unique(w),             1)), zeroone = isTRUE(all.equal(unique(w + abs(w -             1)), 1)), case = isTRUE(all.equal(unique(w - floor(w)),             0)))    }, nuisance = function ()     return(NA), response = function (f)     f, rclass = function (f)     NA, name = "Squared Error (Regression)", charloss = "(y - f)^2 \n"),     control = ctrl)
#> 
#> 
#> 	 Squared Error (Regression) 
#> 
#> Loss function: (y - f)^2 
#>  
#> 
#> Number of boosting iterations: mstop = 100 
#> Step size:  0.1 
#> Offset:  19.78571 
#> 
#> Coefficients: 
#>  (Intercept)           am         carb          cyl         disp         drat 
#> 10.351144270  1.653982491 -0.206433252 -0.621226267  0.001713983  0.505328191 
#>           hp         qsec           wt 
#> -0.017400354  0.159539470 -2.756708400 
#> attr(,"offset")
#> [1] 19.78571
#> 


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

# Score the predictions
predictions$score()
#> regr.mse 
#> 7.093802