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
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	-		-

Offset

If a Task contains a column with the offset role, it is automatically incorporated via the offset argument in mboost's training function. No offset is applied during prediction for this learner.

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$base_learner()
• mlr3::Learner$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()

---

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: offset, 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:  21.00476 
#> 
#> Coefficients: 
#> (Intercept)          am        carb         cyl          hp        qsec 
#> 13.03985271  1.72951619 -0.13213496 -0.37517651 -0.02126749  0.22048206 
#>          vs          wt 
#> -0.47304443 -3.85453254 
#> attr(,"offset")
#> [1] 21.00476
#> 


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

# Score the predictions
predictions$score()
#> regr.mse 
#>  7.06443