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
#>
#> $penalty
#> [1] 2
#>
#> $degree
#> [1] 1
#>
#> $nk
#> [1] 21
#>
#> $thresh
#> [1] 0.001
#>
#> $gcv
#> [1] 10.27583
#>
#> $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 1 0 0 0 0 0 0 0 0
#> [9,] 0 -1 0 0 0 0 0 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 1 0 0 0 0 0
#> [13,] 0 0 0 0 -1 0 0 0 0 0
#> [14,] 0 0 1 0 0 0 0 0 0 0
#> [15,] 0 0 -1 0 0 0 0 0 0 0
#> [16,] 0 0 0 0 0 0 0 1 0 0
#> [17,] 0 0 0 0 0 0 0 -1 0 0
#> [18,] 0 0 0 1 0 0 0 0 0 0
#> [19,] 0 0 0 -1 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 1.513
#> [3,] 0 0 0 0.0 0.00 0 0 0.0 0 1.513
#> [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 1 0 0.0 0.00 0 0 0.0 0 0.000
#> [9,] 0 1 0 0.0 0.00 0 0 0.0 0 0.000
#> [10,] 0 0 0 0.0 0.00 3 0 0.0 0 0.000
#> [11,] 0 0 0 0.0 0.00 3 0 0.0 0 0.000
#> [12,] 0 0 0 0.0 2.76 0 0 0.0 0 0.000
#> [13,] 0 0 0 0.0 2.76 0 0 0.0 0 0.000
#> [14,] 0 0 4 0.0 0.00 0 0 0.0 0 0.000
#> [15,] 0 0 4 0.0 0.00 0 0 0.0 0 0.000
#> [16,] 0 0 0 0.0 0.00 0 0 15.5 0 0.000
#> [17,] 0 0 0 0.0 0.00 0 0 15.5 0 0.000
#> [18,] 0 0 0 75.7 0.00 0 0 0.0 0 0.000
#> [19,] 0 0 0 75.7 0.00 0 0 0.0 0 0.000
#>
#> $residuals
#> [,1]
#> [1,] -2.4055237
#> [2,] -2.0519550
#> [3,] 0.8632318
#> [4,] -0.7519447
#> [5,] -4.5251578
#> [6,] 3.7426959
#> [7,] -1.6519447
#> [8,] -0.7537277
#> [9,] -0.3251423
#> [10,] 0.5137879
#> [11,] 4.4328943
#> [12,] 6.9694725
#> [13,] 2.1489313
#> [14,] -2.6528465
#> [15,] -3.5662297
#> [16,] 1.7007376
#> [17,] 0.5917914
#> [18,] 0.2801862
#> [19,] 1.6571447
#> [20,] -2.9823080
#> [21,] -1.2340936
#>
#> $fitted.values
#> [,1]
#> [1,] 23.405524
#> [2,] 24.851955
#> [3,] 20.536768
#> [4,] 19.451945
#> [5,] 18.825158
#> [6,] 20.657304
#> [7,] 19.451945
#> [8,] 18.053728
#> [9,] 10.725142
#> [10,] 9.886212
#> [11,] 10.267106
#> [12,] 25.430528
#> [13,] 28.251069
#> [14,] 24.152847
#> [15,] 19.066230
#> [16,] 17.499262
#> [17,] 26.708209
#> [18,] 25.719814
#> [19,] 28.742855
#> [20,] 22.682308
#> [21,] 22.634094
#>
#> $lenb
#> [1] 19
#>
#> $coefficients
#> [,1]
#> [1,] 28.742855
#> [2,] -4.821438
#>
#> $x
#> [,1] [,2]
#> [1,] 1 1.107
#> [2,] 1 0.807
#> [3,] 1 1.702
#> [4,] 1 1.927
#> [5,] 1 2.057
#> [6,] 1 1.677
#> [7,] 1 1.927
#> [8,] 1 2.217
#> [9,] 1 3.737
#> [10,] 1 3.911
#> [11,] 1 3.832
#> [12,] 1 0.687
#> [13,] 1 0.102
#> [14,] 1 0.952
#> [15,] 1 2.007
#> [16,] 1 2.332
#> [17,] 1 0.422
#> [18,] 1 0.627
#> [19,] 1 0.000
#> [20,] 1 1.257
#> [21,] 1 1.267
#>
#> attr(,"class")
#> [1] "mars"
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 12.17846