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 3 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 = 26.72842
fv = 12.25684
fv = 1.340781e+154
fv = 27.02031
fv = 1.340781e+154
fv = 34.63835
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 67.28523
fv = 12.25684
fv = 1.340781e+154
fv = 1157.469
fv = 34.63835
fv = 1.340781e+154
fv = 44.32702
fv = 44.32702
fv = 426.313
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.702617
fv = 13.95445
fv = 1.340781e+154
fv = 8.169511
fv = 432.3
fv = 12.96643
fv = 11.08856
fv = 11281571325
fv = 8722889894
fv = 1.340781e+154
fv = 6.91849
fv = 1.340781e+154
fv = 1.796108e+12
fv = 6.91849
fv = 6.91849
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.91849
fv = 7.696196
fv = 6.629325
fv = 1.340781e+154
fv = 1.340781e+154
fv = 12.19261
fv = 7.737218
fv = 6.601346
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.77184
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.75682
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.300307
fv = 6.522333
fv = 1.340781e+154
fv = 8.059334
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 = 9.494871
fv = 7.183312
fv = 7.330447
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 19.25107
fv = 10.03516
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7183.19
fv = 1.340781e+154
fv = 8.059334
fv = 516.9614
fv = 818.6586
fv = 8.080245
fv = 1.340781e+154
fv = 11.65209
fv = 8.236435
fv = 1.340781e+154
fv = 6.300307
fv = 6.522333
fv = 6.131592
fv = 1.340781e+154
fv = 8.247437
fv = 6.131592
fv = 6.131592
fv = 7.131249
fv = 1967.793
fv = 1.340781e+154
fv = 10.02127
fv = 1.340781e+154
fv = 11.28009
fv = 5.949707
fv = 7.595482
fv = 1.340781e+154
fv = 8.247437
fv = 9029.366
fv = 5.949707
fv = 7.595482
fv = 11.38642
fv = 7.761382
fv = 6.729638
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.949707
fv = 6.819823
fv = 13.32214
fv = 1.340781e+154
fv = 5.949707
fv = 1.340781e+154
fv = 14.31244
fv = 11.87204
fv = 1.340781e+154
fv = 8.896071
fv = 746.253
fv = 5.949707
fv = 72560.47
fv = 1.340781e+154
fv = 7.916997
fv = 5.949707
fv = 11.73705
fv = 7.839873
fv = 1.340781e+154
fv = 7.380312
fv = 1.340781e+154
fv = 8.787365
fv = 8.579119
fv = 8.146493
fv = 6.021105
fv = 9.11811
fv = 6.142533
fv = 1.340781e+154
fv = 6.131592
fv = 11.51007
fv = 5.949707
fv = 5.949707
fv = 11.51007
fv = 5.949707
fv = 6.131592
fv = 9.021052
fv = 1.340781e+154
fv = 6.300307
fv = 5.949707
fv = 1.340781e+154
fv = 7.595482
fv = 7.965022
fv = 6.300307
fv = 6.400671
fv = 6.726472
fv = 6.881773
fv = 5.949707
fv = 5.949707
fv = 5.949707
fv = 6.643488
fv = 1.340781e+154
fv = 7.209735
fv = 6.131592
fv = 11.73916
run # 0: f=5.949706698
#>
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 = 7.737437
fv = 37.87295
fv = 1.340781e+154
fv = 1.340781e+154
fv = 34.35616
fv = 7.697961
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 = 7.697961
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 = 31.64577
fv = 1.340781e+154
fv = 1.340781e+154
fv = 13.03987
fv = 1.340781e+154
fv = 137934270557
fv = 7.697961
fv = 7.697961
fv = 49.60774
fv = 12.66047
fv = 1.340781e+154
fv = 20.24331
fv = 20.24331
fv = 32.80419
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 = 9.108499
fv = 10.19163
fv = 10.6152
fv = 85.55636
fv = 33.41663
fv = 66.09475
fv = 7.697961
fv = 15063.18
fv = 1.340781e+154
fv = 1.340781e+154
fv = 14.45034
fv = 2705.723
fv = 37.87295
fv = 32.80419
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.858337
fv = 11.08929
fv = 1.340781e+154
fv = 24.05282
fv = 11.14258
fv = 9.581272
fv = 7.697961
fv = 7.697961
fv = 7.697961
fv = 7.697961
fv = 1.340781e+154
fv = 8.569362
fv = 7.697961
fv = 6.173398
fv = 6352580
fv = 1.340781e+154
fv = 36.60444
fv = 36.60444
fv = 8.865102
fv = 1.340781e+154
fv = 41.28117
fv = 6.173398
fv = 6.173398
fv = 6.173398
fv = 1.340781e+154
fv = 6.173398
fv = 6.173398
fv = 8.712489
fv = 7.936711
fv = 11.30047
fv = 11.30047
fv = 10.80734
fv = 14.94852
fv = 11.30047
fv = 9.196445
fv = 9.196445
fv = 34.35616
fv = 8605897
fv = 163.9443
fv = 1.340781e+154
fv = 157.1151
fv = 7.034743
fv = 11.40621
fv = 14.81832
fv = 6.310384
fv = 12.00009
fv = 7.785883
fv = 6.173398
fv = 6.173398
fv = 6.173398
fv = 6.901795
fv = 1.340781e+154
fv = 6.173398
fv = 6.173398
fv = 6.173398
fv = 9.672075
run # 1: f=6.173397739
#>
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 = 5.95201
fv = 37.87295
fv = 1.340781e+154
fv = 1.340781e+154
fv = 26.62643
fv = 5.95201
fv = 7.438213
fv = 1.340781e+154
fv = 7.577606
fv = 9.137548
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 352943059946
fv = 13.82927
fv = 374605919137
fv = 1.340781e+154
fv = 12.91331
fv = 18.24465
fv = 5.719613
fv = 5.616853
fv = 5.616853
fv = 32.80419
fv = 5.901514
fv = 6.524983
fv = 8.438987
fv = 11.00634
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.616853
fv = 37481.89
fv = 3614177503
fv = 305800845523
fv = 1.340781e+154
fv = 1007976110
fv = 5.616853
fv = 1.340781e+154
fv = 7932232
fv = 6.524983
fv = 5.616853
fv = 86.44213
fv = 1.340781e+154
fv = 13.05615
fv = 276.8809
fv = 141640.8
fv = 1.340781e+154
fv = 5.616853
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.616853
fv = 1.340781e+154
fv = 1114.683
fv = 5.719613
fv = 443.4356
fv = 5.719613
fv = 1.340781e+154
fv = 1.340781e+154
fv = 107.9963
fv = 1.340781e+154
fv = 12.97326
fv = 5.616853
fv = 1.340781e+154
fv = 12.77529
fv = 12.70719
fv = 5.616853
fv = 1.340781e+154
fv = 7.177947
fv = 6.931262
fv = 8.7677
fv = 7.352464
fv = 5.616853
fv = 5.616853
fv = 6.010371
fv = 5.616853
fv = 1.340781e+154
fv = 5.616853
fv = 5.616853
fv = 5.616853
fv = 6.239156
fv = 5.616853
fv = 5.95201
fv = 45.96374
fv = 7.398619
fv = 5.616853
fv = 8.661063
fv = 8.593387
fv = 7.221679
fv = 5.616853
fv = 6.145414
fv = 39.02157
fv = 7.405823
fv = 7.726913
fv = 305447.1
fv = 1.340781e+154
fv = 5.616853
fv = 6.752115
fv = 1.340781e+154
fv = 5.901514
fv = 5.96391
fv = 6.967179
fv = 6.410097
fv = 5.616853
fv = 1.340781e+154
fv = 6.642782
fv = 6.524983
fv = 5.616853
fv = 5.616853
fv = 5.616853
fv = 5.616853
fv = 6.750164
fv = 10.97478
run # 2: f=5.616852518
#>
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 # 3: f=1.340780793e+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 = 78.99423
fv = 37.87295
fv = 1.340781e+154
fv = 1.340781e+154
fv = 34.35616
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 22.31509
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 = 1.529961e+13
fv = 15.25276
fv = 104.7544
fv = 1.340781e+154
fv = 1.340781e+154
fv = 889799805
fv = 12.04904
fv = 1459472
fv = 2513.607
fv = 48660857
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 = 204.6001
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 13.21407
fv = 94684383
fv = 14.32873
fv = 1.340781e+154
fv = 24.88942
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 = 22.16929
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 12.04904
fv = 1.340781e+154
fv = 11.29828
fv = 27.48067
fv = 40.76063
fv = 1.340781e+154
fv = 535007731
fv = 1.340781e+154
fv = 11.29828
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.29828
fv = 1.340781e+154
fv = 8.597908
fv = 8.597908
fv = 1.340781e+154
fv = 1.340781e+154
fv = 10.50179
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.83502
fv = 34.35616
fv = 1.340781e+154
fv = 140.9892
fv = 12.94458
fv = 7.238673
fv = 1.340781e+154
fv = 9.032058
fv = 7.238673
fv = 7.643511
fv = 1.340781e+154
fv = 1.340781e+154
fv = 125.3519
fv = 6.659756
fv = 1.173905e+13
fv = 1.340781e+154
fv = 39.24492
fv = 470.2753
fv = 1.340781e+154
fv = 6.659756
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 = 14.06813
fv = 1.340781e+154
fv = 19.18838
fv = 31.72816
fv = 1.340781e+154
fv = 16.43952
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 = 28.17465
fv = 28.17465
fv = 44.93533
fv = 7.270145
fv = 6.659756
fv = 1.340781e+154
fv = 23.35572
fv = 35.33825
fv = 27.22399
fv = 6.659756
fv = 12.15687
fv = 1.340781e+154
fv = 54.65523
fv = 9.34732
fv = 46.62047
fv = 16.96554
fv = 65.07652
fv = 1.340781e+154
fv = 305387.8
fv = 205258555
fv = 18208.31
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 17.41262
fv = 16.63058
fv = 303.6718
fv = 83469.71
fv = 6.659756
fv = 39.27751
fv = 792.1719
fv = 295.7817
fv = 72.04511
fv = 5962.592
fv = 79.14535
fv = 6.659756
fv = 1.340781e+154
fv = 6.659756
fv = 39.19015
fv = 1.340781e+154
fv = 17.89489
fv = 1.340781e+154
fv = 14.29281
fv = 24421741
fv = 12.36201
fv = 1.340781e+154
fv = 80.11631
fv = 11.40968
fv = 1.340781e+154
#> Warning: number of rows of result is not a multiple of vector length (arg 2)
#>
fv = 10.10942
fv = 1.340781e+154
fv = 27.98013
fv = 118.8996
fv = 47.85501
fv = 35.76031
fv = 71.97603
fv = 1652.298
fv = 6.659756
fv = 6.256073
fv = 2311.544
fv = 1.340781e+154
fv = 22.58335
fv = 6.256073
fv = 5.079932
fv = 4.721331
fv = 1.340781e+154
fv = 7.503974
fv = 1.340781e+154
fv = 3.969078
fv = 417.9764
fv = 1.340781e+154
fv = 8.551054
fv = 5.885283
fv = 4.83679
fv = 15.37307
fv = 11.56385
fv = 1.340781e+154
fv = 3.969078
fv = 3.969078
fv = 3.969078
fv = 7.274634
fv = 24.82188
fv = 26.00519
fv = 17.69003
fv = 22633.78
fv = 1479.895
fv = 5.429966
fv = 23.05801
fv = 1437.638
fv = 124446
fv = 1.340781e+154
fv = 2070.004
fv = 33.67333
fv = 18.60347
fv = 3.969078
fv = 5.147238
fv = 3.969078
fv = 3.045069
fv = 4.419081
fv = 1.340781e+154
fv = 3755.08
fv = 14.73649
fv = 3.045069
fv = 3.045069
fv = 3.045069
fv = 3.045069
fv = 3.778183
fv = 3.969078
fv = 26.78821
fv = 4.106132
fv = 3.363109
fv = 4.419081
fv = 5.192558
fv = 4.204468
fv = 9.11986
fv = 18.21505
fv = 4.476139
fv = 1471.448
fv = 7.934788
run # 4: f=3.045069052
#>
#> bb eval : 741
#> best : 3.045069052
#> worst : 1.340780793e+154
#> solution: x = ( 0 0 1 0 1 0 1 1 0 2 1 2 1 4 1 1 3 5 1 1 ) f(x) = 3.045069052
#>
#>
fv = 3.045069 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...
# Score the predictions
predictions$score()
#> regr.mse
#> 6.96899