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 4
#>
#> $penalty
#> [1] 2
#>
#> $degree
#> [1] 1
#>
#> $nk
#> [1] 21
#>
#> $thresh
#> [1] 0.001
#>
#> $gcv
#> [1] 8.466947
#>
#> $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 1 0 0 0 0 0
#> [13,] 0 0 0 0 -1 0 0 0 0 0
#> [14,] 0 0 0 0 0 0 0 0 1 0
#> [15,] 0 0 0 0 0 0 0 0 -1 0
#> [16,] 0 1 0 0 0 0 0 0 0 0
#> [17,] 0 -1 0 0 0 0 0 0 0 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 1.835
#> [3,] 0 0 0 0.0 0.00 0 0 0.0 0 1.835
#> [4,] 0 0 0 0.0 0.00 0 62 0.0 0 0.000
#> [5,] 0 0 0 0.0 0.00 0 62 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 0 0 71.1 0.00 0 0 0.0 0 0.000
#> [11,] 0 0 0 71.1 0.00 0 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 0 0.0 0.00 0 0 0.0 0 0.000
#> [15,] 0 0 0 0.0 0.00 0 0 0.0 0 0.000
#> [16,] 0 1 0 0.0 0.00 0 0 0.0 0 0.000
#> [17,] 0 1 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,] -1.81180368
#> [2,] -0.05350329
#> [3,] 0.93157094
#> [4,] -2.58905100
#> [5,] 0.05144307
#> [6,] 0.78912090
#> [7,] 0.44385264
#> [8,] -2.19738823
#> [9,] 1.36274467
#> [10,] -0.99580566
#> [11,] 1.14846103
#> [12,] 2.27223723
#> [13,] 5.00552816
#> [14,] 5.00448813
#> [15,] -3.50699395
#> [16,] -3.02044395
#> [17,] 0.13009338
#> [18,] -1.15314715
#> [19,] 0.76786999
#> [20,] -0.74507983
#> [21,] -1.83419339
#>
#> $fitted.values
#> [,1]
#> [1,] 22.811804
#> [2,] 21.453503
#> [3,] 17.768429
#> [4,] 20.689051
#> [5,] 14.248557
#> [6,] 23.610879
#> [7,] 22.356147
#> [8,] 19.997388
#> [9,] 15.037255
#> [10,] 16.195806
#> [11,] 9.251539
#> [12,] 8.127763
#> [13,] 27.394472
#> [14,] 28.895512
#> [15,] 25.006994
#> [16,] 18.520444
#> [17,] 13.169907
#> [18,] 28.453147
#> [19,] 15.032130
#> [20,] 20.445080
#> [21,] 23.234193
#>
#> $lenb
#> [1] 19
#>
#> $coefficients
#> [,1]
#> [1,] 29.0241057
#> [2,] -3.9950012
#> [3,] -0.0428646
#>
#> $x
#> [,1] [,2] [,3]
#> [1,] 1 1.040 48
#> [2,] 1 1.380 48
#> [3,] 1 1.605 113
#> [4,] 1 1.625 43
#> [5,] 1 1.735 183
#> [6,] 1 1.355 0
#> [7,] 1 1.315 33
#> [8,] 1 1.605 61
#> [9,] 1 2.235 118
#> [10,] 1 1.945 118
#> [11,] 1 3.415 143
#> [12,] 1 3.589 153
#> [13,] 1 0.365 4
#> [14,] 1 0.000 3
#> [15,] 1 0.630 35
#> [16,] 1 1.685 88
#> [17,] 1 2.005 183
#> [18,] 1 0.100 4
#> [19,] 1 1.335 202
#> [20,] 1 0.935 113
#> [21,] 1 0.945 47
#>
#> attr(,"class")
#> [1] "mars"
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 11.3324