Regression Mars Learner
mlr_learners_regr.mars.Rd
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
Examples
# Define the Learner
learner = mlr3::lrn("regr.mars")
print(learner)
#> <LearnerRegrMars:regr.mars>: Multivariate Adaptive Regression Splines
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, mda
#> * Predict Types: [response]
#> * Feature Types: integer, numeric
#> * Properties: -
# Define a Task
task = mlr3::tsk("mtcars")
# Create train and test set
ids = mlr3::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 6 8 10
#>
#> $penalty
#> [1] 2
#>
#> $degree
#> [1] 1
#>
#> $nk
#> [1] 21
#>
#> $thresh
#> [1] 0.001
#>
#> $gcv
#> [1] 10.47619
#>
#> $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 1 0 0 0 0 0 0 0
#> [3,] 0 0 -1 0 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 1 0 0 0 0 0 0 0 0
#> [7,] 0 -1 0 0 0 0 0 0 0 0
#> [8,] 1 0 0 0 0 0 0 0 0 0
#> [9,] -1 0 0 0 0 0 0 0 0 0
#> [10,] 0 0 0 0 0 0 0 1 0 0
#> [11,] 0 0 0 0 0 0 0 -1 0 0
#> [12,] 0 0 0 0 0 1 0 0 0 0
#> [13,] 0 0 0 0 0 -1 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 1 0 0 0 0 0 0
#> [17,] 0 0 0 -1 0 0 0 0 0 0
#> [18,] 0 0 0 0 0 0 0 0 1 0
#> [19,] 0 0 0 0 0 0 0 0 -1 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 4 0.0 0.00 0 0 0.0 0 0.000
#> [3,] 0 0 4 0.0 0.00 0 0 0.0 0 0.000
#> [4,] 0 0 0 0.0 0.00 0 0 0.0 0 1.615
#> [5,] 0 0 0 0.0 0.00 0 0 0.0 0 1.615
#> [6,] 0 1 0 0.0 0.00 0 0 0.0 0 0.000
#> [7,] 0 1 0 0.0 0.00 0 0 0.0 0 0.000
#> [8,] 0 0 0 0.0 0.00 0 0 0.0 0 0.000
#> [9,] 0 0 0 0.0 0.00 0 0 0.0 0 0.000
#> [10,] 0 0 0 0.0 0.00 0 0 14.5 0 0.000
#> [11,] 0 0 0 0.0 0.00 0 0 14.5 0 0.000
#> [12,] 0 0 0 0.0 0.00 3 0 0.0 0 0.000
#> [13,] 0 0 0 0.0 0.00 3 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 71.1 0.00 0 0 0.0 0 0.000
#> [17,] 0 0 0 71.1 0.00 0 0 0.0 0 0.000
#> [18,] 0 0 0 0.0 0.00 0 0 0.0 0 0.000
#> [19,] 0 0 0 0.0 0.00 0 0 0.0 0 0.000
#>
#> $residuals
#> [,1]
#> [1,] -2.4420909
#> [2,] 0.2544881
#> [3,] 2.0144579
#> [4,] -3.9507822
#> [5,] 2.2865906
#> [6,] -0.7098876
#> [7,] 2.2351455
#> [8,] 0.9247394
#> [9,] -0.9716224
#> [10,] -4.0970998
#> [11,] 0.8528379
#> [12,] 1.1605000
#> [13,] 1.9143834
#> [14,] 2.1614407
#> [15,] -0.3070556
#> [16,] -1.0114516
#> [17,] 2.4796398
#> [18,] -0.3733192
#> [19,] -4.7173714
#> [20,] 1.3246468
#> [21,] 0.9718106
#>
#> $fitted.values
#> [,1]
#> [1,] 23.44209
#> [2,] 21.14551
#> [3,] 16.68554
#> [4,] 22.05078
#> [5,] 12.01341
#> [6,] 23.50989
#> [7,] 15.56485
#> [8,] 15.47526
#> [9,] 16.17162
#> [10,] 14.49710
#> [11,] 13.84716
#> [12,] 31.23950
#> [13,] 28.48562
#> [14,] 31.73856
#> [15,] 21.80706
#> [16,] 16.51145
#> [17,] 16.72036
#> [18,] 26.37332
#> [19,] 20.51737
#> [20,] 18.37535
#> [21,] 14.02819
#>
#> $lenb
#> [1] 19
#>
#> $coefficients
#> [,1]
#> [1,] 15.412133
#> [2,] -1.651311
#> [3,] 10.059170
#> [4,] 1.160603
#>
#> $x
#> [,1] [,2] [,3] [,4]
#> [1,] 1 3 1 2.52
#> [2,] 1 0 0 4.94
#> [3,] 1 1 0 2.52
#> [4,] 1 0 0 5.72
#> [5,] 1 3 0 1.34
#> [6,] 1 1 0 8.40
#> [7,] 1 3 0 4.40
#> [8,] 1 2 0 2.90
#> [9,] 1 2 0 3.50
#> [10,] 1 3 0 3.48
#> [11,] 1 3 0 2.92
#> [12,] 1 0 1 4.97
#> [13,] 1 1 1 4.02
#> [14,] 1 0 1 5.40
#> [15,] 1 0 0 5.51
#> [16,] 1 1 0 2.37
#> [17,] 1 1 0 2.55
#> [18,] 1 1 1 2.20
#> [19,] 1 3 1 0.00
#> [20,] 1 5 1 1.00
#> [21,] 1 7 1 0.10
#>
#> attr(,"class")
#> [1] "mars"
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 17.85785