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 4 8
#>
#> $penalty
#> [1] 2
#>
#> $degree
#> [1] 1
#>
#> $nk
#> [1] 21
#>
#> $thresh
#> [1] 0.001
#>
#> $gcv
#> [1] 10.58851
#>
#> $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 0 0 0 0 0 0 0 1
#> [5,] 0 0 0 0 0 0 0 0 0 -1
#> [6,] 0 0 1 0 0 0 0 0 0 0
#> [7,] 0 0 -1 0 0 0 0 0 0 0
#> [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 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,] 1 0 0 0 0 0 0 0 0 0
#> [15,] -1 0 0 0 0 0 0 0 0 0
#> [16,] 0 1 0 0 0 0 0 0 0 0
#> [17,] 0 -1 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 0.000
#> [2,] 0 0 0 71.1 0 0 0 0.0 0 0.000
#> [3,] 0 0 0 71.1 0 0 0 0.0 0 0.000
#> [4,] 0 0 0 0.0 0 0 0 0.0 0 1.513
#> [5,] 0 0 0 0.0 0 0 0 0.0 0 1.513
#> [6,] 0 0 4 0.0 0 0 0 0.0 0 0.000
#> [7,] 0 0 4 0.0 0 0 0 0.0 0 0.000
#> [8,] 0 0 0 0.0 0 0 62 0.0 0 0.000
#> [9,] 0 0 0 0.0 0 0 62 0.0 0 0.000
#> [10,] 0 0 0 0.0 0 3 0 0.0 0 0.000
#> [11,] 0 0 0 0.0 0 3 0 0.0 0 0.000
#> [12,] 0 0 0 0.0 0 0 0 14.5 0 0.000
#> [13,] 0 0 0 0.0 0 0 0 14.5 0 0.000
#> [14,] 0 0 0 0.0 0 0 0 0.0 0 0.000
#> [15,] 0 0 0 0.0 0 0 0 0.0 0 0.000
#> [16,] 0 1 0 0.0 0 0 0 0.0 0 0.000
#> [17,] 0 1 0 0.0 0 0 0 0.0 0 0.000
#>
#> $residuals
#> [,1]
#> [1,] -2.38117176
#> [2,] -0.09699892
#> [3,] -0.82120215
#> [4,] 0.97905044
#> [5,] 0.50798224
#> [6,] -1.03644256
#> [7,] 0.23033786
#> [8,] -1.70023328
#> [9,] -0.56108913
#> [10,] 0.41169753
#> [11,] 5.01876124
#> [12,] 6.00248046
#> [13,] -3.03655837
#> [14,] -3.43078601
#> [15,] -4.01881508
#> [16,] 2.32843714
#> [17,] -0.22034439
#> [18,] 0.13224948
#> [19,] 3.25059478
#> [20,] 0.05139794
#> [21,] -1.60934747
#>
#> $fitted.values
#> [,1]
#> [1,] 25.181172
#> [2,] 21.496999
#> [3,] 15.121202
#> [4,] 23.420950
#> [5,] 22.292018
#> [6,] 20.236443
#> [7,] 17.069662
#> [8,] 16.900233
#> [9,] 10.961089
#> [10,] 9.988302
#> [11,] 9.681239
#> [12,] 27.897520
#> [13,] 24.536558
#> [14,] 18.930786
#> [15,] 19.218815
#> [16,] 16.871563
#> [17,] 27.520344
#> [18,] 25.867751
#> [19,] 27.149405
#> [20,] 15.748602
#> [21,] 23.009347
#>
#> $lenb
#> [1] 17
#>
#> $coefficients
#> [,1]
#> [1,] 29.10359369
#> [2,] -3.38857730
#> [3,] -0.03831742
#>
#> $x
#> [,1] [,2] [,3]
#> [1,] 1 0.807 31
#> [2,] 1 1.702 48
#> [3,] 1 2.057 183
#> [4,] 1 1.677 0
#> [5,] 1 1.637 33
#> [6,] 1 1.927 61
#> [7,] 1 2.217 118
#> [8,] 1 2.267 118
#> [9,] 1 3.737 143
#> [10,] 1 3.911 153
#> [11,] 1 3.832 168
#> [12,] 1 0.322 3
#> [13,] 1 0.952 35
#> [14,] 1 2.007 88
#> [15,] 1 1.922 88
#> [16,] 1 2.332 113
#> [17,] 1 0.422 4
#> [18,] 1 0.627 29
#> [19,] 1 0.000 51
#> [20,] 1 1.657 202
#> [21,] 1 1.267 47
#>
#> attr(,"class")
#> [1] "mars"
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 6.514048