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
Methods
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()
Examples
# Define the Learner
learner = 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 = tsk("mtcars")
# Create train and test set
ids = 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 18
#>
#> $selected.terms
#> [1] 1 4 6
#>
#> $penalty
#> [1] 2
#>
#> $degree
#> [1] 1
#>
#> $nk
#> [1] 21
#>
#> $thresh
#> [1] 0.001
#>
#> $gcv
#> [1] 4.462556
#>
#> $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 0 1 0 0 0 0 0 0 0
#> [5,] 0 0 -1 0 0 0 0 0 0 0
#> [6,] 0 0 0 0 0 0 0 0 0 1
#> [7,] 0 0 0 0 0 0 0 0 0 -1
#> [8,] 0 0 0 0 0 0 1 0 0 0
#> [9,] 0 0 0 0 0 0 -1 0 0 0
#> [10,] 0 0 0 0 0 0 0 0 1 0
#> [11,] 0 0 0 0 0 0 0 0 -1 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 1 0 0 0 0 0 0 0 0
#> [15,] 0 -1 0 0 0 0 0 0 0 0
#> [16,] 0 0 0 0 0 1 0 0 0 0
#> [17,] 0 0 0 0 0 -1 0 0 0 0
#> [18,] 1 0 0 0 0 0 0 0 0 0
#> [19,] -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 0 0 0.0 0 0.000
#> [2,] 0 0 0 79 0 0 0 0.0 0 0.000
#> [3,] 0 0 0 79 0 0 0 0.0 0 0.000
#> [4,] 0 0 4 0 0 0 0 0.0 0 0.000
#> [5,] 0 0 4 0 0 0 0 0.0 0 0.000
#> [6,] 0 0 0 0 0 0 0 0.0 0 1.935
#> [7,] 0 0 0 0 0 0 0 0.0 0 1.935
#> [8,] 0 0 0 0 0 0 66 0.0 0 0.000
#> [9,] 0 0 0 0 0 0 66 0.0 0 0.000
#> [10,] 0 0 0 0 0 0 0 0.0 0 0.000
#> [11,] 0 0 0 0 0 0 0 0.0 0 0.000
#> [12,] 0 0 0 0 0 0 0 14.5 0 0.000
#> [13,] 0 0 0 0 0 0 0 14.5 0 0.000
#> [14,] 0 1 0 0 0 0 0 0.0 0 0.000
#> [15,] 0 1 0 0 0 0 0 0.0 0 0.000
#> [16,] 0 0 0 0 0 3 0 0.0 0 0.000
#> [17,] 0 0 0 0 0 3 0 0.0 0 0.000
#> [18,] 0 0 0 0 0 0 0 0.0 0 0.000
#> [19,] 0 0 0 0 0 0 0 0.0 0 0.000
#>
#> $residuals
#> [,1]
#> [1,] 0.28600906
#> [2,] 0.84697460
#> [3,] -1.51286747
#> [4,] -1.38520226
#> [5,] 0.31302034
#> [6,] -1.11010177
#> [7,] 1.81473015
#> [8,] -0.02323065
#> [9,] -1.58942935
#> [10,] -1.20665287
#> [11,] 2.91955781
#> [12,] -0.29519551
#> [13,] -0.78218402
#> [14,] -1.79123876
#> [15,] 4.11976056
#> [16,] 2.14018457
#> [17,] 1.29115686
#> [18,] -0.76514820
#> [19,] -0.68401121
#> [20,] -0.68520226
#> [21,] -1.90092965
#>
#> $fitted.values
#> [,1]
#> [1,] 20.71399
#> [2,] 20.15303
#> [3,] 24.31287
#> [4,] 15.68520
#> [5,] 22.48698
#> [6,] 18.91010
#> [7,] 14.58527
#> [8,] 15.22323
#> [9,] 11.98943
#> [10,] 11.60665
#> [11,] 11.78044
#> [12,] 15.79520
#> [13,] 15.98218
#> [14,] 15.09124
#> [15,] 15.08024
#> [16,] 25.15982
#> [17,] 24.70884
#> [18,] 16.56515
#> [19,] 20.38401
#> [20,] 15.68520
#> [21,] 23.30093
#>
#> $lenb
#> [1] 19
#>
#> $coefficients
#> [,1]
#> [1,] 25.159815
#> [2,] -1.469459
#> [3,] -2.199865
#>
#> $x
#> [,1] [,2] [,3]
#> [1,] 1 2 0.685
#> [2,] 1 2 0.940
#> [3,] 1 0 0.385
#> [4,] 1 4 1.635
#> [5,] 1 0 1.215
#> [6,] 1 2 1.505
#> [7,] 1 4 2.135
#> [8,] 1 4 1.845
#> [9,] 1 4 3.315
#> [10,] 1 4 3.489
#> [11,] 1 4 3.410
#> [12,] 1 4 1.585
#> [13,] 1 4 1.500
#> [14,] 1 4 1.905
#> [15,] 1 4 1.910
#> [16,] 1 0 0.000
#> [17,] 1 0 0.205
#> [18,] 1 4 1.235
#> [19,] 1 2 0.835
#> [20,] 1 4 1.635
#> [21,] 1 0 0.845
#>
#> attr(,"class")
#> [1] "mars"
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 19.88186