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, mda
#> * Predict Types: [response]
#> * Feature Types: integer, numeric
#> * Properties: -
# 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 10 12
#>
#> $penalty
#> [1] 2
#>
#> $degree
#> [1] 1
#>
#> $nk
#> [1] 21
#>
#> $thresh
#> [1] 0.001
#>
#> $gcv
#> [1] 6.311181
#>
#> $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 0 1 0 0 0 0 0 0
#> [3,] 0 0 0 -1 0 0 0 0 0 0
#> [4,] 0 1 0 0 0 0 0 0 0 0
#> [5,] 0 -1 0 0 0 0 0 0 0 0
#> [6,] 0 0 0 0 0 1 0 0 0 0
#> [7,] 0 0 0 0 0 -1 0 0 0 0
#> [8,] 0 0 0 0 1 0 0 0 0 0
#> [9,] 0 0 0 0 -1 0 0 0 0 0
#> [10,] 0 0 1 0 0 0 0 0 0 0
#> [11,] 0 0 -1 0 0 0 0 0 0 0
#> [12,] 0 0 0 0 0 0 0 0 0 1
#> [13,] 0 0 0 0 0 0 0 0 0 -1
#> [14,] 0 0 0 0 0 0 1 0 0 0
#> [15,] 0 0 0 0 0 0 -1 0 0 0
#> [16,] 1 0 0 0 0 0 0 0 0 0
#> [17,] -1 0 0 0 0 0 0 0 0 0
#>
#> $cuts
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [1,] 0 0 0 0 0.00 0 0 0 0 0.000
#> [2,] 0 0 0 79 0.00 0 0 0 0 0.000
#> [3,] 0 0 0 79 0.00 0 0 0 0 0.000
#> [4,] 0 1 0 0 0.00 0 0 0 0 0.000
#> [5,] 0 1 0 0 0.00 0 0 0 0 0.000
#> [6,] 0 0 0 0 0.00 3 0 0 0 0.000
#> [7,] 0 0 0 0 0.00 3 0 0 0 0.000
#> [8,] 0 0 0 0 2.76 0 0 0 0 0.000
#> [9,] 0 0 0 0 2.76 0 0 0 0 0.000
#> [10,] 0 0 4 0 0.00 0 0 0 0 0.000
#> [11,] 0 0 4 0 0.00 0 0 0 0 0.000
#> [12,] 0 0 0 0 0.00 0 0 0 0 1.513
#> [13,] 0 0 0 0 0.00 0 0 0 0 1.513
#> [14,] 0 0 0 0 0.00 0 62 0 0 0.000
#> [15,] 0 0 0 0 0.00 0 62 0 0 0.000
#> [16,] 0 0 0 0 0.00 0 0 0 0 0.000
#> [17,] 0 0 0 0 0.00 0 0 0 0 0.000
#>
#> $residuals
#> [,1]
#> [1,] 0.5059363
#> [2,] -1.8483833
#> [3,] -1.6886192
#> [4,] 1.9336070
#> [5,] 0.2332856
#> [6,] 0.1229760
#> [7,] -1.2770240
#> [8,] 1.7126664
#> [9,] -1.3751207
#> [10,] -0.9387227
#> [11,] 3.1631426
#> [12,] -2.7847183
#> [13,] -0.6140209
#> [14,] -1.1272039
#> [15,] 3.9010904
#> [16,] 1.6860233
#> [17,] 3.7276326
#> [18,] -1.1918331
#> [19,] -1.0574073
#> [20,] -0.9886192
#> [21,] -2.0946873
#>
#> $fitted.values
#> [,1]
#> [1,] 20.49406
#> [2,] 24.64838
#> [3,] 15.98862
#> [4,] 22.46639
#> [5,] 22.56671
#> [6,] 19.07702
#> [7,] 19.07702
#> [8,] 15.58733
#> [9,] 11.77512
#> [10,] 11.33872
#> [11,] 11.53686
#> [12,] 24.28472
#> [13,] 16.11402
#> [14,] 16.32720
#> [15,] 15.29891
#> [16,] 25.61398
#> [17,] 26.67237
#> [18,] 16.99183
#> [19,] 20.75741
#> [20,] 15.98862
#> [21,] 23.49469
#>
#> $lenb
#> [1] 17
#>
#> $coefficients
#> [,1]
#> [1,] 26.672367
#> [2,] -1.381180
#> [3,] -2.508035
#>
#> $x
#> [,1] [,2] [,3]
#> [1,] 1 2 1.362
#> [2,] 1 0 0.807
#> [3,] 1 4 2.057
#> [4,] 1 0 1.677
#> [5,] 1 0 1.637
#> [6,] 1 2 1.927
#> [7,] 1 2 1.927
#> [8,] 1 4 2.217
#> [9,] 1 4 3.737
#> [10,] 1 4 3.911
#> [11,] 1 4 3.832
#> [12,] 1 0 0.952
#> [13,] 1 4 2.007
#> [14,] 1 4 1.922
#> [15,] 1 4 2.332
#> [16,] 1 0 0.422
#> [17,] 1 0 0.000
#> [18,] 1 4 1.657
#> [19,] 1 2 1.257
#> [20,] 1 4 2.057
#> [21,] 1 0 1.267
#>
#> attr(,"class")
#> [1] "mars"
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 13.93663