Regression Mars Learner
mlr_learners_regr.mars.Rd
Multivariate Adaptive Regression Splines.
Calls mda::mars()
from mda.
Meta Information
Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3extralearners, mda
Parameters
Id | Type | Default | Levels | Range |
degree | integer | 1 | \([1, \infty)\) | |
nk | integer | - | \([1, \infty)\) | |
penalty | numeric | 2 | \([0, \infty)\) | |
thresh | numeric | 0.001 | \([0, \infty)\) | |
prune | logical | TRUE | TRUE, FALSE | - |
trace.mars | logical | FALSE | TRUE, FALSE | - |
forward.step | logical | FALSE | TRUE, FALSE | - |
References
Friedman, H J (1991). “Multivariate adaptive regression splines.” The annals of statistics, 19(1), 1–67.
See also
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.
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrMars
Examples
# Define the Learner
learner = mlr3::lrn("regr.mars")
print(learner)
#>
#> ── <LearnerRegrMars> (regr.mars): Multivariate Adaptive Regression Splines ─────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3extralearners, and mda
#> • Predict Types: [response]
#> • Feature Types: integer and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties:
#> • Other settings: use_weights = 'error'
# 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)
#> $call
#> mda::mars(x = x, y = y)
#>
#> $all.terms
#> [1] 1 2 4 6 8 10 12 14 16
#>
#> $selected.terms
#> [1] 1 2 4
#>
#> $penalty
#> [1] 2
#>
#> $degree
#> [1] 1
#>
#> $nk
#> [1] 21
#>
#> $thresh
#> [1] 0.001
#>
#> $gcv
#> [1] 8.072613
#>
#> $factor
#> am carb cyl disp drat gear hp qsec vs wt
#> [1,] 0 0 0 0 0 0 0 0 0 0
#> [2,] 0 0 1 0 0 0 0 0 0 0
#> [3,] 0 0 -1 0 0 0 0 0 0 0
#> [4,] 0 0 0 0 0 0 0 0 0 1
#> [5,] 0 0 0 0 0 0 0 0 0 -1
#> [6,] 0 1 0 0 0 0 0 0 0 0
#> [7,] 0 -1 0 0 0 0 0 0 0 0
#> [8,] 1 0 0 0 0 0 0 0 0 0
#> [9,] -1 0 0 0 0 0 0 0 0 0
#> [10,] 0 0 0 0 0 1 0 0 0 0
#> [11,] 0 0 0 0 0 -1 0 0 0 0
#> [12,] 0 0 0 0 0 0 0 1 0 0
#> [13,] 0 0 0 0 0 0 0 -1 0 0
#> [14,] 0 0 0 0 1 0 0 0 0 0
#> [15,] 0 0 0 0 -1 0 0 0 0 0
#> [16,] 0 0 0 1 0 0 0 0 0 0
#> [17,] 0 0 0 -1 0 0 0 0 0 0
#>
#> $cuts
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [1,] 0 0 0 0.0 0.00 0 0 0.0 0 0.000
#> [2,] 0 0 4 0.0 0.00 0 0 0.0 0 0.000
#> [3,] 0 0 4 0.0 0.00 0 0 0.0 0 0.000
#> [4,] 0 0 0 0.0 0.00 0 0 0.0 0 1.935
#> [5,] 0 0 0 0.0 0.00 0 0 0.0 0 1.935
#> [6,] 0 1 0 0.0 0.00 0 0 0.0 0 0.000
#> [7,] 0 1 0 0.0 0.00 0 0 0.0 0 0.000
#> [8,] 0 0 0 0.0 0.00 0 0 0.0 0 0.000
#> [9,] 0 0 0 0.0 0.00 0 0 0.0 0 0.000
#> [10,] 0 0 0 0.0 0.00 3 0 0.0 0 0.000
#> [11,] 0 0 0 0.0 0.00 3 0 0.0 0 0.000
#> [12,] 0 0 0 0.0 0.00 0 0 14.5 0 0.000
#> [13,] 0 0 0 0.0 0.00 0 0 14.5 0 0.000
#> [14,] 0 0 0 0.0 2.76 0 0 0.0 0 0.000
#> [15,] 0 0 0 0.0 2.76 0 0 0.0 0 0.000
#> [16,] 0 0 0 78.7 0.00 0 0 0.0 0 0.000
#> [17,] 0 0 0 78.7 0.00 0 0 0.0 0 0.000
#>
#> $residuals
#> [,1]
#> [1,] -0.933896486
#> [2,] 2.466853217
#> [3,] -1.792568279
#> [4,] -1.617226942
#> [5,] -1.553940141
#> [6,] -2.141171331
#> [7,] 1.697849372
#> [8,] 1.771597479
#> [9,] -1.434570528
#> [10,] -1.011723971
#> [11,] 3.096293971
#> [12,] 5.737414863
#> [13,] -0.538734573
#> [14,] -1.045297546
#> [15,] -1.961085732
#> [16,] 3.951065031
#> [17,] -0.006575583
#> [18,] -0.808394295
#> [19,] -1.089287992
#> [20,] -1.869373591
#> [21,] -0.917226942
#>
#> $fitted.values
#> [,1]
#> [1,] 21.93390
#> [2,] 16.23315
#> [3,] 19.89257
#> [4,] 15.91723
#> [5,] 24.35394
#> [6,] 19.94117
#> [7,] 14.70215
#> [8,] 15.52840
#> [9,] 11.83457
#> [10,] 11.41172
#> [11,] 11.60371
#> [12,] 26.66259
#> [13,] 16.03873
#> [14,] 16.24530
#> [15,] 15.26109
#> [16,] 15.24893
#> [17,] 27.30658
#> [18,] 26.80839
#> [19,] 16.88929
#> [20,] 21.56937
#> [21,] 15.91723
#>
#> $lenb
#> [1] 17
#>
#> $coefficients
#> [,1]
#> [1,] 27.306576
#> [2,] -1.854012
#> [3,] -2.430153
#>
#> $x
#> [,1] [,2] [,3]
#> [1,] 1 2 0.685
#> [2,] 1 4 1.505
#> [3,] 1 2 1.525
#> [4,] 1 4 1.635
#> [5,] 1 0 1.215
#> [6,] 1 2 1.505
#> [7,] 1 4 2.135
#> [8,] 1 4 1.795
#> [9,] 1 4 3.315
#> [10,] 1 4 3.489
#> [11,] 1 4 3.410
#> [12,] 1 0 0.265
#> [13,] 1 4 1.585
#> [14,] 1 4 1.500
#> [15,] 1 4 1.905
#> [16,] 1 4 1.910
#> [17,] 1 0 0.000
#> [18,] 1 0 0.205
#> [19,] 1 4 1.235
#> [20,] 1 2 0.835
#> [21,] 1 4 1.635
#>
#> attr(,"class")
#> [1] "mars"
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 10.19735