Categorical Regression Splines.
Calls crs::crs() from crs.
Parameters
| Id | Type | Default | Levels | Range |
| degree | integer | 3 | \([0, \infty)\) | |
| segments | integer | 1 | \([1, \infty)\) | |
| include | integer | - | \((-\infty, \infty)\) | |
| lambda | untyped | - | - | |
| lambda.discrete | logical | FALSE | TRUE, FALSE | - |
| lambda.discrete.num | integer | 100 | \([0, \infty)\) | |
| cv | character | nomad | nomad, exhaustive, none | - |
| cv.threshold | integer | 10000 | \([0, \infty)\) | |
| cv.func | character | cv.ls | cv.ls, cv.gcv, cv.aic | - |
| kernel | logical | TRUE | TRUE, FALSE | - |
| degree.max | integer | 10 | \([0, \infty)\) | |
| segments.max | integer | 10 | \([1, \infty)\) | |
| degree.min | integer | 0 | \([0, \infty)\) | |
| segments.min | integer | 1 | \([1, \infty)\) | |
| cv.df.min | integer | 1 | \((-\infty, \infty)\) | |
| complexity | character | degree-knots | degree-knots, degree, knots | - |
| knots | character | quantiles | quantiles, uniform, auto | - |
| basis | character | auto | auto, additive, tensor, glp | - |
| prune | logical | FALSE | TRUE, FALSE | - |
| restarts | integer | 0 | \([0, \infty)\) | |
| nmulti | integer | 5 | \([0, \infty)\) | |
| singular.ok | logical | FALSE | TRUE, FALSE | - |
| deriv | integer | 0 | \([0, \infty)\) | |
| data.return | logical | FALSE | TRUE, FALSE | - |
| model.return | logical | FALSE | TRUE, FALSE | - |
| random.seed | integer | - | \((-\infty, \infty)\) | |
| tau | numeric | - | \([0, 1]\) | |
| initial.mesh.size.real | untyped | - | - | |
| initial.mesh.size.integer | untyped | - | - | |
| max.bb.eval | untyped | - | - | |
| min.mesh.size.real | untyped | - | - | |
| min.mesh.size.integer | untyped | - | - | |
| min.poll.size.real | untyped | - | - | |
| min.poll.size.integer | untyped | - | - | |
| opts | untyped | - | - |
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 -> LearnerRegrCrs
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.crs")
print(learner)
#>
#> ── <LearnerRegrCrs> (regr.crs): Regression Splines ─────────────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3 and crs
#> • Predict Types: [response] and se
#> • Feature Types: integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: weights
#> • Other settings: use_weights = 'use'
# 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)
#> Calling NOMAD (Nonsmooth Optimization by Mesh Adaptive Direct Search)
#>
#> starting point # 0: ( 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 )
#> starting point # 1: ( 0 0 1 7 5 1 6 0 0 1 4 5 8 7 1 3 6 6 1 1 )
#> starting point # 2: ( 0 0 1 9 8 1 1 3 0 3 8 6 3 6 4 5 2 6 7 7 )
#> starting point # 3: ( 0 2 0 5 1 0 8 8 0 6 3 2 6 2 9 9 8 2 9 9 )
#> starting point # 4: ( 0 1 0 0 6 1 4 5 0 9 7 9 4 3 6 7 5 9 4 3 )
#>
#>
fv = 19.22697
fv = 16.11574
fv = 22806871
fv = 15.15351
fv = 120.984
fv = 1.340781e+154
fv = 1.340781e+154
fv = 22.19383
fv = 16.08919
fv = 1.340781e+154
fv = 1.340781e+154
fv = 22.19383
fv = 1.340781e+154
fv = 15.27071
fv = 54.99952
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 49.13153
fv = 37.69955
fv = 3736836229
fv = 1.340781e+154
fv = 1.340781e+154
fv = 15.15351
fv = 287986.4
fv = 1.340781e+154
fv = 10169.25
fv = 22.19383
fv = 17790.17
fv = 22.19383
fv = 50.28174
fv = 17.18507
fv = 133.9999
fv = 39914.42
fv = 950.6911
fv = 1734.068
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 142.51
fv = 15.15351
fv = 15.15351
fv = 26.9713
fv = 15.15351
fv = 1.340781e+154
fv = 491.9696
fv = 3298.981
fv = 1.340781e+154
fv = 9565.863
fv = 67.04074
fv = 16.62212
fv = 24.62485
fv = 17.68468
fv = 18.78756
fv = 36.11085
fv = 37.69955
fv = 16.37931
fv = 135.0856
fv = 187.3459
fv = 1.340781e+154
fv = 1.340781e+154
fv = 40.71723
fv = 15.15351
fv = 15.15351
fv = 15.15351
fv = 15.15351
fv = 1.340781e+154
fv = 13.87907
fv = 2140.842
fv = 1.340781e+154
fv = 53.51672
fv = 13.87907
fv = 184.9953
fv = 13.87907
fv = 10.02032
fv = 1.340781e+154
fv = 14.96569
fv = 14.01388
fv = 10.44792
fv = 8.989431
fv = 20.39002
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.939598
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.789352
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.789352
fv = 8.53848
fv = 1.340781e+154
fv = 7.939598
fv = 7.939598
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.939598
fv = 1.340781e+154
fv = 1.340781e+154
fv = 14.84056
fv = 8.187795
fv = 1.340781e+154
fv = 3479828
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 12.45739
fv = 11.72448
fv = 452.1957
fv = 14.30474
fv = 16.10773
fv = 16.80735
fv = 7.015852
fv = 2920.583
fv = 1.340781e+154
fv = 14.77033
fv = 1.340781e+154
fv = 12.32921
fv = 11.07361
fv = 16.7264
fv = 7.056498
fv = 7.297454
fv = 7.015852
fv = 9.176012
fv = 31.78303
fv = 9.20893
fv = 7.015852
fv = 7.967655
fv = 7.967655
fv = 11.28431
fv = 7.326879
fv = 371.7839
fv = 7.326879
fv = 1.340781e+154
fv = 1.340781e+154
fv = 2967.383
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.335666
fv = 7.015852
fv = 9.254867
fv = 9.401657
fv = 8.676021
fv = 7.015852
fv = 14.91305
fv = 9.419549
fv = 96.19463
fv = 7.015852
fv = 12.41726
fv = 6.825467
fv = 9.747819
fv = 1.340781e+154
fv = 7.297454
fv = 7.420355
fv = 12.83297
fv = 1.340781e+154
fv = 12.83297
fv = 9.729279
fv = 10.71504
fv = 12.64331
fv = 13.11499
fv = 10.84694
fv = 10.84694
fv = 6.825467
fv = 13.70698
fv = 8.728667
fv = 7.595369
fv = 8.287219
fv = 1.340781e+154
fv = 10.35464
fv = 12.38741
fv = 14.30474
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 19.71297
fv = 11.41365
fv = 6.825467
fv = 6.466667
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9.027505
fv = 8.592627
fv = 7.970031
fv = 8.921923
fv = 6.466667
fv = 1.340781e+154
fv = 10.1022
fv = 6.620669
#> Warning: number of rows of result is not a multiple of vector length (arg 2)
#>
fv = 8.852136
fv = 6.652412
fv = 6.325325
fv = 76.76623
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.573269
fv = 16.09155
fv = 6.325325
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.756242
fv = 6.325325
fv = 7.490024
fv = 8.592627
fv = 7.717533
fv = 7.02456
fv = 6.931428
fv = 7.895304
fv = 7.895304
fv = 7.02456
fv = 7.02456
fv = 18.66785
fv = 28.44483
fv = 15.1961
fv = 56.34349
fv = 1.340781e+154
fv = 1.340781e+154
fv = 18.19459
fv = 6.325325
fv = 6.863328
fv = 6.325325
fv = 7.7298
fv = 6.652412
fv = 8.728667
fv = 10.10462
fv = 8.592627
fv = 7.7298
fv = 26.94618
fv = 9.341558
fv = 11.41475
fv = 7.897505
fv = 9.064377
fv = 10.57956
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.53111
fv = 6.285486
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.285486
fv = 26.86924
fv = 10.54674
fv = 6.285486
fv = 7.897505
fv = 7.006373
fv = 6.285486
fv = 6.285486
fv = 6.285486
fv = 10.06132
fv = 12.86916
fv = 8.80101
fv = 7.970031
fv = 100.958
fv = 11.91262
fv = 6.285486
fv = 7.944287
fv = 1.340781e+154
fv = 25.5967
fv = 12.75074
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 10.27436
#> Warning: number of rows of result is not a multiple of vector length (arg 2)
#>
fv = 1.340781e+154
fv = 7.215262
fv = 1.340781e+154
fv = 7.500633
fv = 6.072623
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9.514987
fv = 1.340781e+154
fv = 6.072623
fv = 1.340781e+154
fv = 7.868621
fv = 9.184334
fv = 10.20694
fv = 9.655878
fv = 6.072623
fv = 12.47569
fv = 7.086028
fv = 6.072623
fv = 8.07537
fv = 21.07888
fv = 6.325325
fv = 6.072623
fv = 6.072623
fv = 7.235185
fv = 1.340781e+154
fv = 1.340781e+154
run # 0: f=6.072622589
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 367.1318
fv = 25.87405
fv = 147.6672
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.10761
fv = 128.1419
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.10761
fv = 1.340781e+154
fv = 38.723
fv = 93.73976
fv = 594437.8
fv = 1.340781e+154
fv = 14.77596
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.10761
fv = 47.65986
fv = 84.21507
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.10761
fv = 1.340781e+154
fv = 61.24277
fv = 1516441
fv = 1.340781e+154
fv = 43.94968
fv = 2032.951
fv = 43.46022
fv = 1.340781e+154
fv = 1.340781e+154
fv = 351134635
fv = 884.4353
fv = 232.6038
fv = 1281.73
fv = 8330502
fv = 1.340781e+154
fv = 12.03145
fv = 141.9774
fv = 11.10761
fv = 77393174313
fv = 84.21507
fv = 1.340781e+154
fv = 11.10761
fv = 185.241
fv = 11.10761
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.10761
fv = 171.9544
fv = 1.340781e+154
fv = 1.340781e+154
fv = 19.14297
fv = 134.9194
fv = 11.66292
fv = 3388.558
fv = 115.7201
fv = 1.340781e+154
fv = 11.10761
fv = 543.0364
fv = 11.10761
fv = 1.340781e+154
fv = 1.340781e+154
fv = 10209964
fv = 1.340781e+154
fv = 11.10761
fv = 11.10761
fv = 19.91848
fv = 1.340781e+154
fv = 30.51313
fv = 12.96697
fv = 11.10761
fv = 11.10761
fv = 22.64568
fv = 11.10761
fv = 20.16616
fv = 1.340781e+154
fv = 11.10761
fv = 11.10761
fv = 84.21507
fv = 14.77596
fv = 12.86163
fv = 293.4331
fv = 553.6545
fv = 31.32867
fv = 11.10761
fv = 28.11459
fv = 543.0364
fv = 478.882
run # 1: f=11.10761299
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 25.38375
fv = 39.5972
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.47942
fv = 12.51142
fv = 2308536
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.031473
fv = 8.031473
fv = 1.340781e+154
fv = 1.340781e+154
fv = 13.13181
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 227215756
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6917481517
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.031473
fv = 1.340781e+154
fv = 1.340781e+154
fv = 13.13181
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9.719995
fv = 1.340781e+154
fv = 77618.35
fv = 11.49281
fv = 1.340781e+154
fv = 14.89642
fv = 31.36847
fv = 1.340781e+154
fv = 47.80928
fv = 15.47467
fv = 1.340781e+154
fv = 661.6988
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.45875
fv = 1.340781e+154
fv = 8.031473
fv = 8.031473
fv = 8.031473
fv = 28.51174
fv = 8.031473
fv = 15.11386
fv = 10.1587
fv = 10.97467
fv = 15.47467
fv = 1.340781e+154
fv = 19.60399
fv = 9.051562
fv = 12.59781
fv = 8.380435
fv = 9.727092
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.031473
fv = 8.031473
fv = 8.031473
fv = 8.031473
fv = 8.031473
fv = 6.5533
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 32713.87
fv = 6.5533
fv = 459.8394
fv = 19.00795
fv = 14.37565
fv = 7.296485
fv = 10.31772
fv = 10.31772
fv = 11.81534
fv = 17.58366
fv = 47.02055
fv = 25.96584
fv = 6.5533
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.12017
fv = 11.76216
fv = 6.5533
fv = 7.89849
fv = 9.548489
fv = 125.8784
fv = 1.340781e+154
fv = 19.54684
fv = 36.71232
fv = 1.340781e+154
fv = 15.19285
fv = 12.54079
fv = 11.58522
fv = 26.75929
fv = 8.265626
fv = 6.843366
fv = 9.59986
fv = 14.75666
fv = 11.45774
fv = 1.340781e+154
fv = 6.5533
fv = 6.5533
fv = 6.5533
fv = 7.770961
fv = 7.247552
fv = 6.5533
fv = 24.62495
fv = 133.3019
fv = 1.340781e+154
fv = 6.5533
fv = 14.37565
fv = 7.921386
fv = 1.340781e+154
fv = 8.265626
fv = 8.944428
fv = 6.5533
fv = 16.11948
run # 2: f=6.553299683
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 39.5972
fv = 39.5972
fv = 1.340781e+154
fv = 1.340781e+154
fv = 39.5972
fv = 39.5972
fv = 39.5972
fv = 39.5972
fv = 39.5972
fv = 1.340781e+154
fv = 39.5972
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1657.151
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9.747819
fv = 17.30808
fv = 31.31588
fv = 39.5972
fv = 9.747819
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 4963.48
fv = 32.95734
fv = 2232.216
fv = 26.94618
fv = 15.50901
fv = 12.83297
fv = 47.05265
fv = 144090.9
fv = 140891468980
fv = 32.95734
fv = 15.79635
fv = 9.747819
fv = 9.747819
fv = 1.340781e+154
fv = 1.340781e+154
fv = 26.67092
fv = 1.340781e+154
fv = 53026.67
fv = 1.340781e+154
fv = 30.49191
fv = 158.8662
fv = 15.79635
fv = 1.340781e+154
fv = 78.5574
fv = 9.747819
fv = 9.747819
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9.747819
fv = 65.69107
fv = 13.92897
fv = 1.340781e+154
fv = 19.99696
fv = 39.5972
fv = 1.340781e+154
fv = 26.67092
fv = 1.340781e+154
fv = 1.340781e+154
fv = 15.79635
fv = 1.340781e+154
fv = 18.07739
fv = 1.340781e+154
fv = 16.65613
fv = 1.340781e+154
fv = 30.1582
fv = 102.5382
fv = 1.340781e+154
fv = 9.747819
fv = 9.747819
fv = 9.747819
fv = 9.747819
fv = 1.340781e+154
fv = 21.33729
fv = 15.50901
fv = 13.05769
fv = 39.5972
run # 3: f=9.747818802
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
run # 4: f=1.340780793e+154
#>
#> bb eval : 710
#> best : 6.072622589
#> worst : 1.340780793e+154
#> solution: x = ( 0 1 0 0 0 1 0 0 0 1 1 2 1 2 2 2 1 3 1 3 ) f(x) = 6.072622589
#>
#>
fv = 6.072623 Working...
print(learner$model)
#> Call:
#> crs.formula(formula = formula, data = data, weights = private$.get_weights(task))
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
#> Working...
#> Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
#>
# Score the predictions
predictions$score()
#> regr.mse
#> 18.97512