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 2 4
#>
#> $penalty
#> [1] 2
#>
#> $degree
#> [1] 1
#>
#> $nk
#> [1] 21
#>
#> $thresh
#> [1] 0.001
#>
#> $gcv
#> [1] 6.788652
#>
#> $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 1 0 0 0 0 0 0 0 0
#> [11,] 0 -1 0 0 0 0 0 0 0 0
#> [12,] 0 0 0 1 0 0 0 0 0 0
#> [13,] 0 0 0 -1 0 0 0 0 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 0 0 0 0 0 1 0
#> [17,] 0 0 0 0 0 0 0 0 -1 0
#> [18,] 0 0 1 0 0 0 0 0 0 0
#> [19,] 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 0.00 0 0 0.0 0 0.000
#> [2,] 0 0 0 0.0 0.00 0 0 0.0 0 1.615
#> [3,] 0 0 0 0.0 0.00 0 0 0.0 0 1.615
#> [4,] 0 0 0 0.0 0.00 0 52 0.0 0 0.000
#> [5,] 0 0 0 0.0 0.00 0 52 0.0 0 0.000
#> [6,] 0 0 0 0.0 0.00 0 0 0.0 0 0.000
#> [7,] 0 0 0 0.0 0.00 0 0 0.0 0 0.000
#> [8,] 0 0 0 0.0 0.00 0 0 14.5 0 0.000
#> [9,] 0 0 0 0.0 0.00 0 0 14.5 0 0.000
#> [10,] 0 1 0 0.0 0.00 0 0 0.0 0 0.000
#> [11,] 0 1 0 0.0 0.00 0 0 0.0 0 0.000
#> [12,] 0 0 0 75.7 0.00 0 0 0.0 0 0.000
#> [13,] 0 0 0 75.7 0.00 0 0 0.0 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 0.0 0.00 0 0 0.0 0 0.000
#> [17,] 0 0 0 0.0 0.00 0 0 0.0 0 0.000
#> [18,] 0 0 4 0.0 0.00 0 0 0.0 0 0.000
#> [19,] 0 0 4 0.0 0.00 0 0 0.0 0 0.000
#>
#> $residuals
#> [,1]
#> [1,] -2.36036443
#> [2,] 0.34017496
#> [3,] 0.58961536
#> [4,] -1.08121738
#> [5,] 1.72413083
#> [6,] 1.00746909
#> [7,] -0.55326680
#> [8,] -1.95326680
#> [9,] 0.50222006
#> [10,] -1.39870490
#> [11,] 0.44394861
#> [12,] 1.45266864
#> [13,] 5.90882050
#> [14,] 1.13732805
#> [15,] -2.95667125
#> [16,] -3.08171176
#> [17,] -0.24627724
#> [18,] 0.05977764
#> [19,] -0.57353387
#> [20,] 2.46223251
#> [21,] -1.42337181
#>
#> $fitted.values
#> [,1]
#> [1,] 25.160364
#> [2,] 21.059825
#> [3,] 18.110385
#> [4,] 15.381217
#> [5,] 22.675869
#> [6,] 21.792531
#> [7,] 19.753267
#> [8,] 19.753267
#> [9,] 16.797780
#> [10,] 16.598705
#> [11,] 9.956051
#> [12,] 8.947331
#> [13,] 26.491180
#> [14,] 29.262672
#> [15,] 24.456671
#> [16,] 18.581712
#> [17,] 27.546277
#> [18,] 25.940222
#> [19,] 16.373534
#> [20,] 12.537767
#> [21,] 22.823372
#>
#> $lenb
#> [1] 19
#>
#> $coefficients
#> [,1]
#> [1,] 29.26267195
#> [2,] -3.98150089
#> [3,] -0.03159389
#>
#> $x
#> [,1] [,2] [,3]
#> [1,] 1 0.705 41
#> [2,] 1 1.600 58
#> [3,] 1 1.825 123
#> [4,] 1 1.955 193
#> [5,] 1 1.575 10
#> [6,] 1 1.535 43
#> [7,] 1 1.825 71
#> [8,] 1 1.825 71
#> [9,] 1 2.115 128
#> [10,] 1 2.165 128
#> [11,] 1 3.635 153
#> [12,] 1 3.809 163
#> [13,] 1 0.585 14
#> [14,] 1 0.000 0
#> [15,] 1 0.850 45
#> [16,] 1 1.905 98
#> [17,] 1 0.320 14
#> [18,] 1 0.525 39
#> [19,] 1 1.555 212
#> [20,] 1 1.955 283
#> [21,] 1 1.165 57
#>
#> attr(,"class")
#> [1] "mars"
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 10.55939