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 = 4.823285
fv = 47.86701
fv = 15.45701
fv = 64504.79
fv = 470.6045
fv = 11.79432
fv = 1884817
fv = 9876.852
fv = 6.569743
fv = 60943.65
fv = 1.340781e+154
fv = 8.757275
fv = 1.340781e+154
fv = 1.340781e+154
fv = 145.3729
fv = 1.340781e+154
fv = 266.0047
fv = 12263.3
fv = 1.340781e+154
fv = 1167.891
fv = 15.82683
fv = 1.340781e+154
fv = 340.1766
fv = 1.340781e+154
fv = 135.6553
fv = 826.131
#> Warning: number of rows of result is not a multiple of vector length (arg 2)
#>
fv = 43.49288
fv = 7.583633
fv = 17.07379
fv = 21.01632
fv = 31.967
fv = 17.96153
fv = 21.99686
fv = 1.340781e+154
fv = 29.19333
fv = 1.340781e+154
fv = 25.15828
fv = 10.09268
fv = 1.340781e+154
fv = 15.15942
fv = 8.887296
fv = 1.340781e+154
fv = 1.340781e+154
fv = 16.08869
run # 0: f=4.823284735
#>
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 = 14.09466
fv = 19.96215
fv = 11.13443
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.131667
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.36121
fv = 1.340781e+154
fv = 8672855
fv = 2101450939
fv = 7.489271
fv = 1.340781e+154
fv = 125626.3
fv = 1.340781e+154
fv = 16.37655
fv = 7.489271
fv = 1.340781e+154
fv = 2.416341e+14
fv = 1.340781e+154
fv = 456.8105
fv = 1.401248e+12
fv = 1.340781e+154
fv = 16.37655
fv = 1.340781e+154
fv = 516070853709
fv = 17.47976
fv = 456.8105
fv = 5678.545
fv = 7.074898
fv = 5.954918
fv = 5.954918
fv = 1.340781e+154
fv = 5.555029
fv = 5.555029
fv = 1411.125
fv = 5.881425
fv = 5.881425
fv = 5.881425
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 32.31774
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 = 46.95001
fv = 1.340781e+154
fv = 888.1462
fv = 11.84319
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 = 5.011918
fv = 1.340781e+154
fv = 1139.594
fv = 6.081874
fv = 6.081874
fv = 6.081874
fv = 1.340781e+154
fv = 5.778418
fv = 1.340781e+154
fv = 7.107486
fv = 5.011918
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.635862
fv = 5.305477
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1282.272
fv = 1.340781e+154
fv = 5.305477
fv = 4042532
fv = 1.340781e+154
fv = 41712270
fv = 1.105209e+13
fv = 1.340781e+154
fv = 10.37939
fv = 1.340781e+154
fv = 6.028283
fv = 7.521103
fv = 1.340781e+154
fv = 5.011918
fv = 5.011918
fv = 5.153592
fv = 5.701132
fv = 5.011918
fv = 70.58156
fv = 16.36331
fv = 19.37614
fv = 78466.94
fv = 14.50804
fv = 1.340781e+154
fv = 5.011918
fv = 5.011918
fv = 80.04555
fv = 1.340781e+154
fv = 7.417211
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8562.859
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.189232
fv = 1.340781e+154
fv = 5.011918
fv = 5.011918
fv = 5.011918
fv = 4.992992
fv = 9.416955
fv = 1.340781e+154
fv = 5.011918
fv = 4.992992
fv = 1.340781e+154
fv = 4.992992
fv = 4.992992
fv = 5.692067
fv = 4.992992
fv = 1.340781e+154
fv = 288.7599
fv = 5.894472
fv = 5.05771
fv = 13.05688
fv = 4.992992
fv = 4.992992
fv = 4.992992
fv = 15.68141
fv = 9.299353
fv = 5.298084
fv = 4.992992
fv = 5.905491
fv = 14.46625
fv = 1.340781e+154
fv = 5.011918
fv = 6.181323
fv = 6.770038
fv = 1.340781e+154
fv = 8.247015
fv = 5.932239
fv = 1.340781e+154
fv = 8.358748
fv = 6.463588
fv = 14.4016
fv = 4.992992
fv = 5.316247
fv = 4.992992
fv = 4.992992
fv = 4.992992
fv = 4.992992
fv = 4.992992
fv = 4.992992
fv = 4.992992
fv = 19.96215
run # 1: f=4.992991569
#>
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 = 265.3666
fv = 19.96215
fv = 1.340781e+154
fv = 1.340781e+154
fv = 978721.4
fv = 19.96215
fv = 1.340781e+154
fv = 1.340781e+154
fv = 19.96215
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.239747
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 = 4.012571e+13
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 2.73585e+13
fv = 156057241385
fv = 2.73585e+13
fv = 2.73585e+13
fv = 836930512
fv = 1.340781e+154
fv = 26.6474
fv = 1.340781e+154
fv = 24.56696
fv = 16.96769
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 2.691459e+14
fv = 7.396604e+13
fv = 9402373510
fv = 9.093146e+12
fv = 9.093146e+12
fv = 19.06501
fv = 8.239747
fv = 8.239747
fv = 8.239747
fv = 1.340781e+154
fv = 8.239747
fv = 1.340781e+154
fv = 7.749256e+13
fv = 7.75896e+13
fv = 43.03367
fv = 19.37614
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 = 7.401326
fv = 7.834226
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.819999
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.032085
fv = 1.340781e+154
fv = 1.340781e+154
fv = 46.84628
fv = 10.58969
fv = 118.3018
fv = 8.28528
fv = 548.2529
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.401326
fv = 7.401326
fv = 7.401326
fv = 7.401326
fv = 16.62572
fv = 18.52811
fv = 520355364448
fv = 11.29424
fv = 8.423692
fv = 9.303285
fv = 5.638801
fv = 536.3933
fv = 1.340781e+154
fv = 6.287943
fv = 173.412
fv = 1.340781e+154
fv = 7.215271
fv = 7.692816
fv = 5.638801
fv = 1.340781e+154
fv = 6.316472
fv = 5.961779
fv = 5.638801
fv = 5.638801
fv = 5.785117
fv = 20.25904
fv = 5.638801
fv = 3402.459
fv = 1.340781e+154
fv = 17.00618
fv = 7.378275
fv = 5.090734
fv = 13.21571
fv = 1.340781e+154
fv = 1.340781e+154
fv = 73.54691
fv = 1.340781e+154
fv = 11.76444
fv = 5.638801
fv = 5.090734
fv = 28.21938
fv = 4.765052
fv = 49312.58
fv = 1.340781e+154
fv = 5.56323
fv = 25.00173
fv = 11.33224
fv = 1.340781e+154
fv = 11.16283
fv = 1.340781e+154
fv = 53.91589
fv = 4.765052
fv = 4.765052
fv = 9.036312
fv = 4.765052
fv = 4.765052
fv = 7.245192
fv = 4.826114
fv = 15.50334
fv = 12.81975
fv = 5.816212
fv = 12.81975
fv = 12.81975
fv = 5.660719
fv = 1.340781e+154
fv = 10.80822
fv = 10.74597
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.703633
fv = 69.82005
fv = 9.271412
fv = 8.494537
fv = 11.37564
fv = 1.340781e+154
fv = 8.494537
#> Warning: number of rows of result is not a multiple of vector length (arg 2)
#>
fv = 20.98642
fv = 1.340781e+154
fv = 8.494537
fv = 8.494537
fv = 18.85454
fv = 8.546509
fv = 4.765052
fv = 4.765052
fv = 4.765052
fv = 5.350799
#> Warning: number of rows of result is not a multiple of vector length (arg 2)
#>
fv = 12.9772
fv = 11.29424
fv = 171.1753
fv = 1.340781e+154
fv = 8.153
fv = 1.340781e+154
fv = 4.497783
fv = 4856.99
fv = 72.11127
fv = 4.308956
fv = 1.340781e+154
fv = 5.785117
fv = 4.497783
fv = 4.54371
fv = 7.167178
fv = 4.308956
fv = 4.242767
fv = 5.676983
fv = 1.340781e+154
fv = 5.449679
fv = 6.431881
fv = 6.424715
fv = 1.340781e+154
fv = 2.888192
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.54219
fv = 6.203013
fv = 3.765528
fv = 4.723384
fv = 2.888192
fv = 1.340781e+154
fv = 2.564699
fv = 1.340781e+154
fv = 9.933299
fv = 3.729925
fv = 2.829574
fv = 2.564699
fv = 4.070463
fv = 3.870716
fv = 2.564699
fv = 4.371176
fv = 2.940917
fv = 1.340781e+154
fv = 3.046452
fv = 2.564699
fv = 2.564699
fv = 15.87742
fv = 4.051011
fv = 3.749644
fv = 5.471414
fv = 2.328489
fv = 1.340781e+154
fv = 16.07091
fv = 1.340781e+154
fv = 3.857376
fv = 2.551456
fv = 3.521121
fv = 11.0805
fv = 8.613828
fv = 3.194076
fv = 3.801357
fv = 12.0844
fv = 3.478486
fv = 3.708588
fv = 2.328489
fv = 2.328489
fv = 3.000679
fv = 2.944512
fv = 4.205512
fv = 2.358723
fv = 2.564699
fv = 4.64874
fv = 3.305518
fv = 6.732137
fv = 1.340781e+154
fv = 2.328489
fv = 2.328489
fv = 5.143487
fv = 14.54318
fv = 8.366212
fv = 7.196296
fv = 1.340781e+154
fv = 1.340781e+154
fv = 17.11739
fv = 9.877203
fv = 2.328489
fv = 2.551456
fv = 3.708588
fv = 3.521121
fv = 7.367291
fv = 19.29036
fv = 1.340781e+154
fv = 4.806299
fv = 2.822757
fv = 48.5044
fv = 1.340781e+154
fv = 2.70337
fv = 13.82045
fv = 8.927249
run # 2: f=2.328489196
#>
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 # 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 = 23.68626
fv = 19.96215
fv = 8.239747
fv = 8.197086
fv = 1.340781e+154
fv = 6.696172
fv = 6.696172
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 = 7.685416
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 = 13.471
fv = 8.786035
fv = 6.696172
fv = 6.696172
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 = 6.696172
fv = 10.3381
fv = 1.340781e+154
fv = 524.0918
fv = 9.474433
fv = 296.5888
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.696172
fv = 6.696172
fv = 356.8866
fv = 1.340781e+154
fv = 10.3381
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.696172
fv = 6.696172
fv = 6.696172
fv = 6.696172
fv = 6.696172
fv = 14.95802
fv = 13.7665
fv = 9.710872
fv = 78.16258
fv = 4.791401
fv = 799265.1
fv = 1.340781e+154
fv = 20.64572
fv = 2.097957
fv = 1.340781e+154
fv = 1.340781e+154
fv = 20.64572
fv = 2.097957
fv = 2.097957
fv = 2.097957
fv = 1.340781e+154
fv = 48.27719
fv = 19.74551
fv = 1.340781e+154
fv = 2.097957
fv = 22.11393
fv = 1.453388
fv = 1.340781e+154
fv = 6.696172
fv = 1.453388
fv = 1.340781e+154
fv = 84.37355
fv = 1.453388
fv = 5.718843
fv = 11.79122
fv = 25.76524
fv = 11.24796
fv = 1.32323
fv = 17.63024
fv = 1.340781e+154
fv = 6.696172
fv = 1.32323
fv = 8.118769
fv = 1.340781e+154
fv = 41.15356
fv = 2.141044
fv = 17.63024
fv = 11.97471
fv = 14.02419
fv = 19.39522
fv = 21.68781
fv = 15.77661
fv = 1.340781e+154
fv = 60.90263
fv = 18.61679
fv = 21.28828
fv = 430.0166
fv = 1.32323
fv = 2216.564
fv = 17.63024
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.32323
fv = 1.32323
fv = 3.25713
fv = 7.924336
fv = 1.32323
fv = 3.376275
fv = 1.340647
fv = 1.340781e+154
fv = 1.32323
fv = 17.63024
fv = 1.453388
fv = 13.3559
fv = 1.717447
fv = 1.340781e+154
fv = 20.67568
fv = 4.109727
fv = 2.113099
fv = 6.974788
fv = 2.649362
fv = 8.364607
fv = 62.58991
fv = 1.32323
fv = 400.1009
fv = 92.29527
fv = 1.340781e+154
fv = 7.685416
fv = 25.91286
fv = 2.010721
fv = 1.340781e+154
fv = 1.32323
fv = 1.32323
fv = 13.08743
fv = 1.340781e+154
fv = 1.32323
fv = 6.974788
fv = 78.53602
fv = 2.211487
fv = 38.16988
run # 4: f=1.32323031
#>
#> bb eval : 767
#> best : 1.32323031
#> worst : 1.340780793e+154
#> solution: x = ( 0 4 2 0 0 0 2 1 0 0 1 1 1 2 1 1 3 1 1 1 ) f(x) = 1.32323031
#>
#>
fv = 1.32323
#> Warning: optimal degree equals search maximum (1): rerun with larger degree.max optimal degree equals search maximum (4): rerun with larger degree.max optimal degree equals search maximum (2): rerun with larger degree.max optimal degree equals search maximum (10): rerun with larger degree.max optimal degree equals search maximum (10): rerun with larger degree.max optimal degree equals search maximum (2): rerun with larger degree.max optimal degree equals search maximum (10): rerun with larger degree.max optimal degree equals search maximum (10): rerun with larger degree.max optimal degree equals search maximum (1): rerun with larger degree.max optimal degree equals search maximum (10): rerun with larger degree.max
#> 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
#> Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
#> Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
#>
# Score the predictions
predictions$score()
#> regr.mse
#> 62.83352