Classification Generalized Additive Model Learner

Generalized additive models. Calls mgcv::gam() from package mgcv.

Multiclass classification is not implemented yet.

Formula

A gam formula specific to the task at hand is required for the formula parameter (see example and ?mgcv::formula.gam). Beware, if no formula is provided, a fallback formula is used that will make the gam behave like a glm (this behavior is required for the unit tests). Only features specified in the formula will be used, superseding columns with col_roles "feature" in the task.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("classif.gam")
lrn("classif.gam")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, mlr3extralearners, mgcv

Parameters

Id	Type	Default	Levels	Range
formula	untyped	-		-
offset	untyped	NULL		-
method	character	GCV.Cp	GCV.Cp, GACV.Cp, REML, P-REML, ML, P-ML	-
optimizer	untyped	c("outer", "newton")		-
scale	numeric	0		\((-\infty, \infty)\)
select	logical	FALSE	TRUE, FALSE	-
knots	untyped	NULL		-
sp	untyped	NULL		-
min.sp	untyped	NULL		-
H	untyped	NULL		-
gamma	numeric	1		\([1, \infty)\)
paraPen	untyped	NULL		-
G	untyped	NULL		-
in.out	untyped	NULL		-
drop.unused.levels	logical	TRUE	TRUE, FALSE	-
drop.intercept	logical	FALSE	TRUE, FALSE	-
nthreads	integer	1		\([1, \infty)\)
irls.reg	numeric	0		\([0, \infty)\)
epsilon	numeric	1e-07		\([0, \infty)\)
maxit	integer	200		\((-\infty, \infty)\)
trace	logical	FALSE	TRUE, FALSE	-
mgcv.tol	numeric	1e-07		\([0, \infty)\)
mgcv.half	integer	15		\([0, \infty)\)
rank.tol	numeric	1.490116e-08		\([0, \infty)\)
nlm	untyped	list()		-
optim	untyped	list()		-
newton	untyped	list()		-
outerPIsteps	integer	0		\([0, \infty)\)
idLinksBases	logical	TRUE	TRUE, FALSE	-
scalePenalty	logical	TRUE	TRUE, FALSE	-
efs.lspmax	integer	15		\([0, \infty)\)
efs.tol	numeric	0.1		\([0, \infty)\)
scale.est	character	fletcher	fletcher, pearson, deviance	-
edge.correct	logical	FALSE	TRUE, FALSE	-
nei	untyped	-		-
ncv.threads	integer	1		\([1, \infty)\)
block.size	integer	1000		\((-\infty, \infty)\)
unconditional	logical	FALSE	TRUE, FALSE	-

References

Hastie, J T, Tibshirani, J R (2017). Generalized additive models. Routledge.

Wood, Simon (2012). “mgcv: Mixed GAM Computation Vehicle with GCV/AIC/REML smoothness estimation.”

Author

JazzyPierrot

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifGam

Methods

Public methods

• LearnerClassifGam$new()

• LearnerClassifGam$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$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()

Creates a new instance of this R6 class.

Usage

LearnerClassifGam$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifGam$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# simple example
t = mlr3::tsk("spam")
l = mlr3::lrn("classif.gam")
l$param_set$values$formula = type ~ s(george) + s(charDollar) + s(edu) + ti(george, edu)
l$train(t)
l$model
#> 
#> Family: binomial 
#> Link function: logit 
#> 
#> Formula:
#> type ~ s(george) + s(charDollar) + s(edu) + ti(george, edu)
#> 
#> Estimated degrees of freedom:
#> 1.00 5.42 1.00 5.05  total = 13.47 
#> 
#> UBRE score: -0.1759489