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 18
#>
#> $selected.terms
#> [1] 1 2
#>
#> $penalty
#> [1] 2
#>
#> $degree
#> [1] 1
#>
#> $nk
#> [1] 21
#>
#> $thresh
#> [1] 0.001
#>
#> $gcv
#> [1] 10.16009
#>
#> $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,] 0 0 0 0 1 0 0 0 0 0
#> [7,] 0 0 0 0 -1 0 0 0 0 0
#> [8,] 0 0 0 1 0 0 0 0 0 0
#> [9,] 0 0 0 -1 0 0 0 0 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 1 0 0 0 0 0 0 0
#> [13,] 0 0 -1 0 0 0 0 0 0 0
#> [14,] 0 0 0 0 0 1 0 0 0 0
#> [15,] 0 0 0 0 0 -1 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,] 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 0.00 0 0 0.00 0 0.000
#> [2,] 0 0 0 0.0 0.00 0 0 0.00 0 1.615
#> [3,] 0 0 0 0.0 0.00 0 0 0.00 0 1.615
#> [4,] 0 0 0 0.0 0.00 0 52 0.00 0 0.000
#> [5,] 0 0 0 0.0 0.00 0 52 0.00 0 0.000
#> [6,] 0 0 0 0.0 2.76 0 0 0.00 0 0.000
#> [7,] 0 0 0 0.0 2.76 0 0 0.00 0 0.000
#> [8,] 0 0 0 71.1 0.00 0 0 0.00 0 0.000
#> [9,] 0 0 0 71.1 0.00 0 0 0.00 0 0.000
#> [10,] 0 1 0 0.0 0.00 0 0 0.00 0 0.000
#> [11,] 0 1 0 0.0 0.00 0 0 0.00 0 0.000
#> [12,] 0 0 4 0.0 0.00 0 0 0.00 0 0.000
#> [13,] 0 0 4 0.0 0.00 0 0 0.00 0 0.000
#> [14,] 0 0 0 0.0 0.00 3 0 0.00 0 0.000
#> [15,] 0 0 0 0.0 0.00 3 0 0.00 0 0.000
#> [16,] 0 0 0 0.0 0.00 0 0 15.41 0 0.000
#> [17,] 0 0 0 0.0 0.00 0 0 15.41 0 0.000
#> [18,] 0 0 0 0.0 0.00 0 0 0.00 0 0.000
#> [19,] 0 0 0 0.0 0.00 0 0 0.00 0 0.000
#>
#> $residuals
#> [,1]
#> [1,] -1.4174404
#> [2,] -2.8924950
#> [3,] 0.9888994
#> [4,] -0.3833758
#> [5,] -0.8653558
#> [6,] -4.0162459
#> [7,] 2.0053344
#> [8,] 0.1166242
#> [9,] 1.0342537
#> [10,] -1.8770361
#> [11,] 1.9974329
#> [12,] 3.0242068
#> [13,] 5.9993851
#> [14,] 0.5473005
#> [15,] 5.3455203
#> [16,] -3.3368501
#> [17,] -3.1112959
#> [18,] -3.4229761
#> [19,] 2.5065289
#> [20,] -0.6643797
#> [21,] -1.5780353
#>
#> $fitted.values
#> [,1]
#> [1,] 22.417440
#> [2,] 25.692495
#> [3,] 20.411101
#> [4,] 19.083376
#> [5,] 18.965356
#> [6,] 18.316246
#> [7,] 20.794666
#> [8,] 19.083376
#> [9,] 15.365746
#> [10,] 17.077036
#> [11,] 8.402567
#> [12,] 7.375793
#> [13,] 26.400615
#> [14,] 29.852700
#> [15,] 28.554480
#> [16,] 24.836850
#> [17,] 18.611296
#> [18,] 16.722976
#> [19,] 16.693471
#> [20,] 27.964380
#> [21,] 22.978035
#>
#> $lenb
#> [1] 19
#>
#> $coefficients
#> [,1]
#> [1,] 29.852700
#> [2,] -5.900999
#>
#> $x
#> [,1] [,2]
#> [1,] 1 1.260
#> [2,] 1 0.705
#> [3,] 1 1.600
#> [4,] 1 1.825
#> [5,] 1 1.845
#> [6,] 1 1.955
#> [7,] 1 1.535
#> [8,] 1 1.825
#> [9,] 1 2.455
#> [10,] 1 2.165
#> [11,] 1 3.635
#> [12,] 1 3.809
#> [13,] 1 0.585
#> [14,] 1 0.000
#> [15,] 1 0.220
#> [16,] 1 0.850
#> [17,] 1 1.905
#> [18,] 1 2.225
#> [19,] 1 2.230
#> [20,] 1 0.320
#> [21,] 1 1.165
#>
#> attr(,"class")
#> [1] "mars"
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 12.18863