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/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.
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', 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)
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] 9.154743
#>
#> $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 0 0 0 0 0 0 1
#> [3,] 0 0 0 0 0 0 0 0 0 -1
#> [4,] 0 0 0 0 0 0 1 0 0 0
#> [5,] 0 0 0 0 0 0 -1 0 0 0
#> [6,] 1 0 0 0 0 0 0 0 0 0
#> [7,] -1 0 0 0 0 0 0 0 0 0
#> [8,] 0 0 0 0 0 0 0 1 0 0
#> [9,] 0 0 0 0 0 0 0 -1 0 0
#> [10,] 0 0 0 1 0 0 0 0 0 0
#> [11,] 0 0 0 -1 0 0 0 0 0 0
#> [12,] 0 0 0 0 0 0 0 0 1 0
#> [13,] 0 0 0 0 0 0 0 0 -1 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 1 0 0 0 0 0 0 0
#> [17,] 0 0 -1 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 0.000
#> [2,] 0 0 0 0.0 0 0 0 0.0 0 1.513
#> [3,] 0 0 0 0.0 0 0 0 0.0 0 1.513
#> [4,] 0 0 0 0.0 0 0 52 0.0 0 0.000
#> [5,] 0 0 0 0.0 0 0 52 0.0 0 0.000
#> [6,] 0 0 0 0.0 0 0 0 0.0 0 0.000
#> [7,] 0 0 0 0.0 0 0 0 0.0 0 0.000
#> [8,] 0 0 0 0.0 0 0 0 14.5 0 0.000
#> [9,] 0 0 0 0.0 0 0 0 14.5 0 0.000
#> [10,] 0 0 0 71.1 0 0 0 0.0 0 0.000
#> [11,] 0 0 0 71.1 0 0 0 0.0 0 0.000
#> [12,] 0 0 0 0.0 0 0 0 0.0 0 0.000
#> [13,] 0 0 0 0.0 0 0 0 0.0 0 0.000
#> [14,] 0 1 0 0.0 0 0 0 0.0 0 0.000
#> [15,] 0 1 0 0.0 0 0 0 0.0 0 0.000
#> [16,] 0 0 4 0.0 0 0 0 0.0 0 0.000
#> [17,] 0 0 4 0.0 0 0 0 0.0 0 0.000
#>
#> $residuals
#> [,1]
#> [1,] -1.7358460307
#> [2,] -2.5436009536
#> [3,] -0.0397142726
#> [4,] -0.0007636503
#> [5,] 0.6783689777
#> [6,] -1.0057367468
#> [7,] -2.4057367468
#> [8,] -2.0599229406
#> [9,] -0.5325144050
#> [10,] 4.8531848148
#> [11,] 1.1822183214
#> [12,] 5.8971350861
#> [13,] -3.1750717190
#> [14,] -1.8499774362
#> [15,] 2.0431580027
#> [16,] -0.2927079006
#> [17,] -0.0876719356
#> [18,] 2.5588280033
#> [19,] -1.3542254141
#> [20,] -1.5549056442
#> [21,] 1.4255025902
#>
#> $fitted.values
#> [,1]
#> [1,] 22.735846
#> [2,] 25.343601
#> [3,] 21.439714
#> [4,] 18.700764
#> [5,] 22.121631
#> [6,] 20.205737
#> [7,] 20.205737
#> [8,] 17.259923
#> [9,] 10.932514
#> [10,] 9.846815
#> [11,] 29.217782
#> [12,] 28.002865
#> [13,] 24.675072
#> [14,] 15.149977
#> [15,] 17.156842
#> [16,] 27.592708
#> [17,] 26.087672
#> [18,] 27.841172
#> [19,] 17.154225
#> [20,] 21.254906
#> [21,] 13.574497
#>
#> $lenb
#> [1] 17
#>
#> $coefficients
#> [,1]
#> [1,] 29.60662121
#> [2,] -3.81215223
#> [3,] -0.02894179
#>
#> $x
#> [,1] [,2] [,3]
#> [1,] 1 1.362 58
#> [2,] 1 0.807 41
#> [3,] 1 1.702 58
#> [4,] 1 1.927 123
#> [5,] 1 1.637 43
#> [6,] 1 1.927 71
#> [7,] 1 1.927 71
#> [8,] 1 2.267 128
#> [9,] 1 3.737 153
#> [10,] 1 3.832 178
#> [11,] 1 0.102 0
#> [12,] 1 0.322 13
#> [13,] 1 0.952 45
#> [14,] 1 2.327 193
#> [15,] 1 2.332 123
#> [16,] 1 0.422 14
#> [17,] 1 0.627 39
#> [18,] 1 0.000 61
#> [19,] 1 1.657 212
#> [20,] 1 1.257 123
#> [21,] 1 2.057 283
#>
#> attr(,"class")
#> [1] "mars"
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 7.976217