Regression Splines Learner

Categorical Regression Splines. Calls crs::crs() from crs.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.crs")

Meta Information

• Task type: “regr”

• Predict Types: “response”, “se”

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

• Required Packages: mlr3, crs

Parameters

Id	Type	Default	Levels	Range
degree	integer	3		\([0, \infty)\)
segments	integer	1		\([1, \infty)\)
include	integer	-		\((-\infty, \infty)\)
lambda	untyped	-		-
lambda.discrete	logical	FALSE	TRUE, FALSE	-
lambda.discrete.num	integer	100		\([0, \infty)\)
cv	character	nomad	nomad, exhaustive, none	-
cv.threshold	integer	1000		\([0, \infty)\)
cv.func	character	cv.ls	cv.ls, cv.gcv, cv.aic	-
kernel	logical	TRUE	TRUE, FALSE	-
degree.max	integer	10		\([0, \infty)\)
segments.max	integer	10		\([1, \infty)\)
degree.min	integer	0		\([0, \infty)\)
segments.min	integer	1		\([1, \infty)\)
cv.df.min	integer	1		\((-\infty, \infty)\)
complexity	character	degree-knots	degree-knots, degree, knots	-
knots	character	quantiles	quantiles, uniform, auto	-
basis	character	auto	auto, additive, tensor, glp	-
prune	logical	FALSE	TRUE, FALSE	-
restarts	integer	0		\([0, \infty)\)
nmulti	integer	5		\([0, \infty)\)
singular.ok	logical	FALSE	TRUE, FALSE	-
deriv	integer	0		\([0, \infty)\)
data.return	logical	FALSE	TRUE, FALSE	-
model.return	logical	FALSE	TRUE, FALSE	-
random.seed	integer	-		\((-\infty, \infty)\)
tau	numeric	-		\([0, 1]\)
initial.mesh.size.real	untyped	-		-
initial.mesh.size.integer	untyped	-		-
max.bb.eval	untyped	-		-
min.mesh.size.real	untyped	-		-
min.mesh.size.integer	untyped	-		-
min.frame.size.real	untyped	-		-
min.frame.size.integer	untyped	-		-
display.nomad.progress	logical	TRUE	TRUE, FALSE	-
display.warnings	logical	TRUE	TRUE, FALSE	-
opts	untyped	-		-

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/chapters/chapter2/data_and_basic_modeling.html#sec-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

annanzrv

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrCrs

Methods

Public methods

• LearnerRegrCrs$new()

• LearnerRegrCrs$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$train()
• mlr3::LearnerRegr$predict_newdata_fast()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrCrs$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrCrs$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("regr.crs", cv = "none")
print(learner)
#> 
#> ── <LearnerRegrCrs> (regr.crs): Regression Splines ─────────────────────────────
#> • Model: -
#> • Parameters: cv=none
#> • Packages: mlr3 and crs
#> • Predict Types: [response] and se
#> • Feature Types: integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: weights
#> • Other settings: use_weights = 'use', predict_raw = 'FALSE'

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

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
#> Warning:  cv="none" selected but no degree provided, using degree=rep(3,num.x): you might consider other degree settings
#> Warning:  cv="none" selected but no segments provided, using segments=rep(1,num.x): you might consider other segment settings
#> Warning:  cv="none" selected, basis="auto" changed to basis="additive": you might consider basis="tensor" etc.
#> Warning: NaNs produced

print(learner$model)
#> Call:
#> crs.formula(formula = formula, cv = "none", data = data, weights = private$.get_weights(task))

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
#> Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
#> Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
#> Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
#> Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
#> Warning: prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases
#> Warning: NaNs produced

# Score the predictions
predictions$score()
#> regr.mse 
#> 80565.21