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 = 32.69205
fv = 17.64607
fv = 1.340781e+154
fv = 29.87872
fv = 1.340781e+154
fv = 28.08558
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 91.2507
fv = 17.64607
fv = 1.340781e+154
fv = 707.1819
fv = 28.08558
fv = 1.340781e+154
fv = 48.42448
fv = 48.42448
fv = 475.5227
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9.338208
fv = 11.06745
fv = 1.340781e+154
fv = 6.219175
fv = 15.56964
fv = 430.3425
fv = 18363.93
fv = 1.340781e+154
fv = 108.1907
fv = 9.368888
fv = 1.340781e+154
fv = 47.3321
fv = 6335407
fv = 33.52718
fv = 8.292381
fv = 9.045004
fv = 6.92407
fv = 193.1874
fv = 12.12063
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 61162.47
fv = 1.340781e+154
fv = 6.887551
fv = 1.340781e+154
fv = 1.340781e+154
fv = 26.51035
fv = 8.660813
fv = 6.234404
fv = 1.340781e+154
fv = 39.5185
fv = 14.53843
fv = 9.550085
fv = 8.241587
fv = 6.219175
fv = 6.92407
fv = 16339.37
fv = 24.33312
fv = 29.12507
fv = 19.62026
fv = 13.47659
fv = 8.923275
fv = 8.306578
fv = 6.219175
fv = 6.92407
fv = 7.752856
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 19.75484
fv = 8.564188
fv = 6.219175
fv = 7.931864
fv = 1.340781e+154
fv = 7.931864
fv = 7.931864
fv = 8.170715
fv = 6.910466
fv = 6.219175
fv = 10.90396
fv = 10.18926
fv = 1.340781e+154
fv = 6.219175
fv = 13.15277
fv = 6.993603
fv = 1.340781e+154
fv = 26.85953
fv = 16.57914
fv = 1.340781e+154
fv = 5.672567
fv = 145.5388
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.647683
fv = 4.238623
fv = 1.340781e+154
fv = 8.861565
fv = 7.087354
fv = 6.347152
fv = 9.638539
fv = 7.457528
fv = 4.238623
fv = 3.073613
fv = 24.5747
fv = 1.340781e+154
fv = 9.118683
fv = 3.073613
fv = 3.483137
fv = 1.340781e+154
fv = 3.483137
fv = 4.588052
fv = 3.073613
fv = 6.347152
fv = 1.340781e+154
fv = 7.108581
fv = 4.395337
fv = 13.13237
fv = 6.377462
fv = 3.073613
fv = 5.506187
fv = 15.19892
fv = 3.836974
fv = 30.53482
fv = 184.3412
fv = 1.340781e+154
fv = 284.5081
fv = 5.47208
fv = 2.907092
fv = 1.340781e+154
fv = 272.4543
fv = 5.049578
fv = 3.161305
fv = 2.907092
fv = 2.907092
fv = 2.907092
fv = 2.907092
fv = 3.252852
fv = 3.252852
fv = 3.489751
fv = 3.738883
fv = 2.907092
fv = 3.372037
fv = 4.529914
fv = 4.575191
fv = 3.159744
fv = 6.56725
fv = 2.907092
fv = 5.990246
fv = 3.377001
fv = 1222.383
run # 0: f=2.907091814
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 55.2391
fv = 5605.256
fv = 1.340781e+154
fv = 1.340781e+154
fv = 94987254
fv = 458750.4
fv = 61.26729
fv = 58.95898
fv = 64.01122
fv = 1.340781e+154
fv = 64.01122
fv = 118.0065
fv = 1.340781e+154
fv = 57.37584
fv = 67.50301
fv = 55.2391
fv = 64.01122
fv = 58.95898
fv = 1.340781e+154
fv = 1.340781e+154
fv = 73.81691
fv = 1.340781e+154
fv = 47.61202
fv = 1.340781e+154
fv = 5161.811
fv = 1.340781e+154
fv = 1.340781e+154
fv = 274.601
fv = 1407.247
fv = 2649.899
fv = 1.340781e+154
fv = 47.61202
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 36985519
fv = 274.601
fv = 1.340781e+154
fv = 1382.382
fv = 1.340781e+154
fv = 1.340781e+154
fv = 274.601
fv = 735952.7
fv = 1.340781e+154
fv = 47.61202
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9664.134
fv = 1.340781e+154
fv = 433.5975
fv = 261.3125
fv = 86170.74
fv = 275.7093
fv = 60.63301
fv = 5.401032
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1146.56
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 47.03275
fv = 1.340781e+154
fv = 5.401032
fv = 61.02973
fv = 5.401032
fv = 1.340781e+154
fv = 4.56103
fv = 1.340781e+154
fv = 275.6187
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 10.24176
fv = 431.0734
fv = 133218.3
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 4.56103
fv = 91.2046
fv = 25066.72
fv = 4.56103
fv = 63.77999
fv = 17.54034
fv = 1.340781e+154
fv = 117.3071
fv = 5.401032
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1369.13
fv = 50.1096
fv = 1.340781e+154
fv = 74.80797
fv = 419.7876
fv = 15.12044
fv = 1.340781e+154
fv = 31.72249
fv = 1.340781e+154
fv = 167807.9
fv = 721.1461
fv = 4.56103
fv = 31.56065
fv = 1.340781e+154
fv = 1.340781e+154
fv = 4.585181
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.14236
fv = 419.7876
fv = 38.12444
fv = 1.340781e+154
fv = 3.655673
fv = 503.124
fv = 1.340781e+154
fv = 1.340781e+154
fv = 74.80797
fv = 3344.8
fv = 3.655673
fv = 171.6427
fv = 1.340781e+154
fv = 5.387571
fv = 185.1931
fv = 1.340781e+154
fv = 1.340781e+154
fv = 12.77128
fv = 1.717792
fv = 1.340781e+154
fv = 861.3097
fv = 47.75391
fv = 250168
fv = 10.38314
fv = 1373.953
fv = 1.340781e+154
fv = 10.38314
fv = 26.03441
fv = 2500.488
fv = 2500.488
fv = 413.9432
fv = 15696.73
fv = 1189253
fv = 225.5451
fv = 1.340781e+154
fv = 1.340781e+154
fv = 93982.87
fv = 1.340781e+154
fv = 1.717792
fv = 390.6168
fv = 23.16025
fv = 1.340781e+154
fv = 1.340781e+154
fv = 83.15153
fv = 116.1735
fv = 3212.336
fv = 801.8747
fv = 272.3754
fv = 44.24521
fv = 1.340781e+154
fv = 1.717792
fv = 591.6974
fv = 1.717792
fv = 7.742728
fv = 17.88591
fv = 63.04934
fv = 1.340781e+154
fv = 9930.659
fv = 1.717792
fv = 227.5139
fv = 1.717792
fv = 1.717792
fv = 41.90545
fv = 1.340781e+154
run # 1: f=1.717792452
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
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 # 2: 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 = 33947.22
fv = 39.447
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9.45254
fv = 9.724585
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 3113.81
fv = 13.28106
fv = 6068.429
fv = 9.86292
fv = 6.624395
fv = 8.737117
fv = 1.340781e+154
fv = 24.02441
fv = 3969.36
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 12.2202
fv = 1.340781e+154
fv = 5.098172
fv = 91.91998
fv = 371727.9
fv = 1.340781e+154
fv = 177.9764
fv = 1.340781e+154
fv = 9.010658
fv = 5.799795
fv = 5.515793
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.624395
fv = 20617.02
fv = 1.340781e+154
fv = 9.010658
fv = 5.799795
fv = 1.340781e+154
fv = 6.333518
fv = 6427842
fv = 1.340781e+154
fv = 1.340781e+154
fv = 87.36217
fv = 5.098172
fv = 168651.1
fv = 12.2879
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.098172
fv = 2455766
fv = 8.304101
fv = 6.382528
fv = 7.300892
fv = 26.57346
fv = 6.090478
fv = 6.090478
fv = 7.379944
fv = 4.47688
fv = 223.4703
fv = 1.340781e+154
fv = 11.47346
fv = 11.4845
fv = 4.256031
fv = 5.190695
fv = 1.340781e+154
fv = 31.90082
fv = 93.97043
fv = 1.340781e+154
fv = 1.340781e+154
fv = 271.0234
fv = 229.3789
fv = 23.87872
fv = 4.805655
fv = 4.805655
fv = 5.299678
fv = 6.371613
fv = 31.89121
fv = 1.340781e+154
fv = 5.730109
fv = 39.447
fv = 1.340781e+154
fv = 24.40241
fv = 6.371613
fv = 6.371613
fv = 85517625
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 10.07627
fv = 4.776692
fv = 4.256031
fv = 5.015256
fv = 1.340781e+154
fv = 10.07627
fv = 999288342005
fv = 36.55747
fv = 8.236453
fv = 1.340781e+154
fv = 16.40553
fv = 8.236453
fv = 4.256031
fv = 4.347052
fv = 4.256031
fv = 4.256031
fv = 10.07627
fv = 21.54889
fv = 7.038905
fv = 4.256031
fv = 455.7912
fv = 1.340781e+154
fv = 20.7576
fv = 4.256031
fv = 6.068895
fv = 6.068895
fv = 6.068895
fv = 8.599882
fv = 4.347052
fv = 6.327721
fv = 5.428246
fv = 5.274907
fv = 8638.222
fv = 1.340781e+154
fv = 4.975564
fv = 4.256031
fv = 4.256031
fv = 4.256031
fv = 6.265946
fv = 6.068895
fv = 6.23613
fv = 1.340781e+154
fv = 24.16942
fv = 9.811451
fv = 6.068895
fv = 4.256031
fv = 1.340781e+154
fv = 21.54889
fv = 5.317056
fv = 4.936273
fv = 65.0586
fv = 4.927877
fv = 4.256031
fv = 4.975564
fv = 4.256031
fv = 4.920452
fv = 4.256031
fv = 4.585485
fv = 12.38979
fv = 6.464911
fv = 8.391858
fv = 1.340781e+154
fv = 15.01008
fv = 1.340781e+154
fv = 8.432899
fv = 7.514457
fv = 6.080297
run # 3: f=4.256030668
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 76.16757
fv = 39.447
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9.622591
fv = 59.90099
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 21.36163
fv = 23.71557
fv = 18.16567
fv = 13.34438
fv = 23.71557
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 21.36163
fv = 21.36163
fv = 13.34438
fv = 15.23918
fv = 14.19683
fv = 11965652
fv = 12206928740
fv = 16.66475
fv = 7.562896
fv = 254.6626
fv = 17.49254
fv = 7.562896
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 10.05719
fv = 97093.97
fv = 13778024
fv = 13778024
fv = 51335507
fv = 10.05697
fv = 9.812665
fv = 9.59307
fv = 10.05697
fv = 302991566588
fv = 1.340781e+154
fv = 7.049464
fv = 1.340781e+154
fv = 103.6717
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.524757
fv = 21.58273
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 216.3634
fv = 1.340781e+154
fv = 7.838907
fv = 7318.636
fv = 5.439108
fv = 5.439108
fv = 1.340781e+154
fv = 5.150329
fv = 11.36776
fv = 1.340781e+154
fv = 13.21307
fv = 5.403086
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.150329
fv = 22.39137
fv = 1.340781e+154
fv = 285.5327
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 17.17172
fv = 16372.63
fv = 18384546
fv = 5.150329
fv = 31.28993
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9.459444
fv = 7.948812
fv = 8.567761
fv = 2414.983
fv = 5.150329
fv = 1.340781e+154
fv = 7.300892
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.752301
fv = 7.752301
fv = 7.066166
fv = 43.18503
fv = 7.752301
fv = 740.7293
fv = 307.6561
fv = 1.340781e+154
fv = 8.072449
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.634908
fv = 7.752301
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1109.343
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.752301
fv = 1.340781e+154
fv = 4.686471
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.786131
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 4.839422
fv = 18.63949
fv = 18.50832
fv = 1.340781e+154
fv = 4.686471
fv = 1.340781e+154
fv = 466.4029
fv = 13.55182
fv = 26.10333
fv = 1.340781e+154
fv = 12.2202
fv = 5.150329
fv = 13.55182
fv = 13.55182
fv = 13.55182
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.786131
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.01801
fv = 4.686471
fv = 6.419258
fv = 1898.074
fv = 1.340781e+154
fv = 6.693145
fv = 5.390217
fv = 4.686471
fv = 211270.9
fv = 5.606652
fv = 4.710003
fv = 4.686471
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1957.076
fv = 4.686471
fv = 12.2202
fv = 1.340781e+154
fv = 8.571797
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.101991
fv = 1.340781e+154
fv = 5.588436
fv = 1107.878
fv = 1223.36
fv = 4.790875
fv = 4.686471
fv = 5.673161
fv = 4.686471
fv = 5.303909
fv = 1.340781e+154
fv = 4.686471
fv = 9.554197
fv = 9.313439
run # 4: f=4.686471417
#>
#> bb eval : 786
#> best : 1.717792452
#> worst : 1.340780793e+154
#> solution: x = ( 0 1 0 7 0 0 1 0 0 2 1 1 6 3 1 3 1 1 1 3 ) f(x) = 1.717792452
#>
#>
fv = 1.717792 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
#>
# Score the predictions
predictions$score()
#> regr.mse
#> 24.47264