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 = 38.359
fv = 38.54218
fv = 15.21363
fv = 61494.9
fv = 1.340781e+154
fv = 22.19228
fv = 19.62632
fv = 264863198
fv = 1.340781e+154
fv = 17.67995
fv = 21.34691
fv = 1.340781e+154
fv = 49.9347
fv = 1.340781e+154
fv = 4146.902
fv = 1.340781e+154
fv = 19.62632
fv = 19.62632
fv = 1.340781e+154
fv = 6146.544
fv = 1.340781e+154
fv = 11.72075
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 48.2174
fv = 1.340781e+154
fv = 1.340781e+154
fv = 12.90123
fv = 1.340781e+154
fv = 11.72075
fv = 1.340781e+154
fv = 11.72075
fv = 26.8767
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 106.965
fv = 1.340781e+154
fv = 140.6233
fv = 5072.595
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 17.07879
fv = 1.340781e+154
fv = 44.37336
fv = 1.340781e+154
fv = 59.43646
fv = 23.12949
fv = 36.34314
fv = 1.340781e+154
fv = 10.04786
fv = 1.340781e+154
fv = 386942.1
fv = 18.54386
fv = 10.04786
fv = 9.361259
fv = 9.361259
fv = 1.340781e+154
fv = 474804.4
fv = 27.20216
fv = 10.67807
fv = 9.782236
fv = 9.922454
fv = 14.57401
fv = 9.361259
fv = 8.456814
fv = 66028.1
fv = 1.340781e+154
fv = 18.78394
fv = 1.340781e+154
fv = 8.457109
fv = 1.340781e+154
fv = 8.457109
fv = 8.457109
fv = 6.332783
fv = 1.340781e+154
fv = 1.340781e+154
fv = 17.37228
fv = 46.39928
fv = 1.340781e+154
fv = 1.340781e+154
fv = 125.9161
fv = 1.340781e+154
fv = 8349.325
fv = 279349.4
fv = 32.73775
fv = 7.690131
fv = 1.340781e+154
fv = 7.460963
fv = 13.3083
fv = 6.332783
fv = 6.332783
fv = 9.679845
fv = 148559.1
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.138617
fv = 11.13989
fv = 125.9161
fv = 27.20841
fv = 122.6093
fv = 2363.532
fv = 5.804947
fv = 14.57824
fv = 1.340781e+154
fv = 13.27774
fv = 8.808672
fv = 6.564601
fv = 930520
fv = 6.564601
fv = 1.340781e+154
fv = 6.119731
fv = 1.340781e+154
fv = 16.87482
fv = 5.804947
fv = 6.150492
fv = 6.150492
fv = 61.29782
fv = 6.150492
fv = 5.804947
fv = 26.55529
fv = 6.564601
fv = 3284.267
fv = 5.804947
fv = 86.67153
fv = 1.340781e+154
fv = 1.340781e+154
fv = 13.65537
fv = 5.910965
fv = 2261.288
fv = 7.786166
fv = 7.522483
fv = 12.54347
fv = 4.129468
fv = 8.145876
fv = 1.340781e+154
fv = 13.01503
fv = 7.988504
fv = 4.145933
fv = 4.129468
fv = 6.601349
fv = 12.24323
fv = 4.129468
fv = 35.8596
fv = 7.988504
fv = 7.676997
fv = 4.129468
fv = 8.994949
fv = 4.129468
fv = 7.988504
fv = 4.186553
fv = 1.340781e+154
fv = 4.002541
fv = 3549.979
fv = 1.340781e+154
fv = 6.085402
fv = 4.129468
fv = 4.145933
fv = 5.280676
fv = 31.50977
fv = 5.470219
fv = 1.340781e+154
fv = 2198.844
fv = 4.002541
fv = 4.002541
fv = 8.460439
fv = 4.837455
fv = 105.8783
fv = 105.8783
fv = 2028.941
fv = 784440.5
fv = 15.81556
fv = 4.401748
fv = 4.002541
fv = 116.9834
fv = 14.70468
fv = 1.340781e+154
fv = 5.619996
fv = 5.41987
fv = 4.97906
fv = 4.614475
fv = 7.811109
fv = 5.280676
fv = 4.002541
fv = 12.24323
fv = 4.002541
fv = 4.002541
fv = 5.627923
fv = 4.432429
fv = 4.002541
fv = 5.167394
fv = 4.002541
fv = 68.38118
fv = 4.002541
fv = 4.287235
fv = 4.628934
fv = 6.961844
fv = 4.628934
fv = 4.628934
fv = 13.22556
fv = 1.340781e+154
fv = 289.8765
fv = 5.619996
fv = 4.002541
fv = 4.002541
fv = 4.614475
fv = 4.145933
fv = 4.628934
fv = 2197.774
fv = 8.198371
run # 0: f=4.002540974
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.08999
fv = 6.265836
fv = 42.7535
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.634039
fv = 29.29037
fv = 7.797433
fv = 11.83296
fv = 6971108
fv = 14.16393
fv = 12.99516
fv = 14.45097
fv = 11.1339
fv = 112883.3
fv = 6.499763
fv = 1.340781e+154
fv = 23.19821
fv = 1.340781e+154
fv = 10.25901
fv = 698080193
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.265836
fv = 15.37079
fv = 1.340781e+154
fv = 11.71641
fv = 8.669139
fv = 6.265836
fv = 6.81339
fv = 6.81339
fv = 8.159036
fv = 5.01611e+11
fv = 6.265836
fv = 6.81339
fv = 8.85521
fv = 6.265836
fv = 6.265836
fv = 6.265836
fv = 293.6522
fv = 8.339822
fv = 6.265836
fv = 305.227
fv = 1.340781e+154
fv = 26124.49
fv = 1.340781e+154
fv = 1.340781e+154
fv = 12.40164
fv = 6.265836
fv = 6.265836
fv = 12.39785
fv = 6.265836
fv = 6.265836
fv = 11.63104
fv = 284.1448
fv = 630.0126
fv = 100.3738
fv = 6.265836
fv = 7.609988
fv = 7.980362
fv = 962.3577
fv = 6.168745
fv = 29.29037
fv = 1.340781e+154
fv = 42.7535
fv = 7.840162
fv = 11.86119
fv = 11.78779
fv = 34.88764
fv = 6.168745
fv = 962.3577
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.827982
fv = 8.844521
fv = 6.168745
fv = 10.25901
fv = 10.54574
fv = 6.326931
fv = 10.20678
fv = 6.168745
fv = 7.885735
fv = 7.885735
fv = 7.885735
fv = 1033.74
fv = 6.168745
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.56014
fv = 5.885101
fv = 947156.3
fv = 1.340781e+154
fv = 7.56014
fv = 5.885101
fv = 10.12059
fv = 6.635021
fv = 19.91291
fv = 6.494575
fv = 6.544864
fv = 9.00948
fv = 9.00948
fv = 9.906421
fv = 9.00948
fv = 7.142427
fv = 7.165069
fv = 7.162332
fv = 10.12059
fv = 5.24729
fv = 1.340781e+154
fv = 8.844521
fv = 8.667668
fv = 6.384186
fv = 6.384186
fv = 5.158404
fv = 5.507339
fv = 1.340781e+154
fv = 9.129545
fv = 5.863299
fv = 477.1509
fv = 5.490537
fv = 5.158404
fv = 5.158404
fv = 9.787735
fv = 5.158404
fv = 6.335432
fv = 29023.41
fv = 6.508149
fv = 1.340781e+154
fv = 6.920496
fv = 7.71799
fv = 5.158404
fv = 6.214232
fv = 5.556559
fv = 193487.7
fv = 6.214232
fv = 6.384186
fv = 5.158404
fv = 6.669292
fv = 5.931623
fv = 14.95861
fv = 5.158404
fv = 4.56401
fv = 18660.3
fv = 1.340781e+154
fv = 511.0337
fv = 6.798689
fv = 6.209817
fv = 688.1999
fv = 338.6214
fv = 6.658537
fv = 12.95806
fv = 5.022764
fv = 6.658537
fv = 4.56401
fv = 4.56401
fv = 5.138873
fv = 4.879833
fv = 1.340781e+154
fv = 6.483627
fv = 9.165306
fv = 45.8835
fv = 328.3699
fv = 8.510799
fv = 4.56401
fv = 1419.319
fv = 12.00276
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.57632
fv = 4.56401
fv = 8.364921
fv = 4.56401
fv = 4.56401
fv = 6.411348
fv = 5.994947
fv = 5.930105
fv = 4.56401
fv = 6.035504
fv = 7.045258
fv = 5.14038
fv = 5.677404
fv = 7.567847
fv = 5.046557
fv = 637.0495
fv = 303.549
fv = 4.879833
run # 1: f=4.564009553
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.86119
fv = 42.7535
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.86119
fv = 42.7535
fv = 5.919505
fv = 6.911772
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.911772
fv = 140.8829
fv = 222.056
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 89998.15
fv = 24533430398
fv = 19610948196
fv = 13386447
fv = 1.340781e+154
fv = 5.919505
fv = 6.215282
fv = 5.919505
fv = 5.919505
fv = 1.340781e+154
fv = 7.848523
fv = 11.86119
fv = 1.340781e+154
fv = 1.340781e+154
fv = 371.6266
fv = 171342.5
fv = 1601.518
fv = 6.896892
fv = 943.4614
fv = 3534.58
fv = 1280537
fv = 5.919505
fv = 5.919505
fv = 640.0071
fv = 19373.58
fv = 640.0071
fv = 640.0071
fv = 423.2408
fv = 635.5898
fv = 5.919505
fv = 5.919505
fv = 1.340781e+154
fv = 12.54185
fv = 8.030825
fv = 1.340781e+154
fv = 7.848037
fv = 3744.725
fv = 10.56962
fv = 5.912897
fv = 53289193
fv = 1.340781e+154
fv = 6.145878
fv = 5.912897
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.912897
fv = 8.102641
fv = 13.38485
fv = 5.912897
fv = 6.957135
fv = 5.912897
fv = 2462.951
fv = 8.38297
fv = 7.372145
fv = 5.912897
fv = 10.80241
fv = 13.28311
fv = 7.626051
fv = 7.626051
fv = 7.458106
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.345469
fv = 1.340781e+154
fv = 1.340781e+154
fv = 471.4529
fv = 8.070776
fv = 8.06459
fv = 1.340781e+154
fv = 7.378248
fv = 5.912897
fv = 5.912897
fv = 8.102641
fv = 5.912897
fv = 5.912897
fv = 9.992755
fv = 5.912897
fv = 5.912897
fv = 7.626051
fv = 5.912897
fv = 21.05184
fv = 6.483627
run # 2: f=5.912897352
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 29200396780
fv = 50473.58
fv = 42.7535
fv = 39.87264
fv = 39.87264
fv = 1.340781e+154
fv = 351641.5
fv = 71.33115
fv = 16.53945
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 22.66632
fv = 1380.169
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 12.26542
fv = 7.790503e+13
fv = 634.3619
fv = 35080.12
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 18.82712
fv = 11.42411
fv = 42.269
fv = 696.1387
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1903.55
fv = 19.55777
fv = 44.28452
fv = 2045.693
fv = 30.49288
fv = 15.71174
fv = 1.340781e+154
fv = 72.04206
fv = 55.90211
fv = 1.340781e+154
fv = 1.340781e+154
fv = 27.2339
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 14.66516
fv = 24.32468
fv = 1015.122
fv = 9.463603
fv = 16.11895
fv = 1.340781e+154
fv = 44.28452
fv = 63.28934
fv = 55.55653
fv = 1.340781e+154
fv = 3164493
fv = 9.463603
fv = 1.340781e+154
fv = 9.463603
fv = 11.29459
fv = 187.149
fv = 1.340781e+154
fv = 10.75296
fv = 1.340781e+154
fv = 20.11238
fv = 1058.512
fv = 180.0468
fv = 11.29459
fv = 1.340781e+154
fv = 1.340781e+154
fv = 28.45068
fv = 23.25321
fv = 1.340781e+154
fv = 1.340781e+154
fv = 44.28452
fv = 1.340781e+154
fv = 45.56714
fv = 379940.5
fv = 20.02339
fv = 12.4651
fv = 1.340781e+154
fv = 12.7874
fv = 12.07299
fv = 1.340781e+154
fv = 9.463603
fv = 12.07299
fv = 7.766438
fv = 1.340781e+154
fv = 142.7328
fv = 9.735804
fv = 9.735804
fv = 9.735804
fv = 7.766438
fv = 16.99216
fv = 13.76511
fv = 9.168974
fv = 7.766438
fv = 2221.413
fv = 10.53365
fv = 9.856796e+13
fv = 10.53365
fv = 15.07364
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9.138861
fv = 13.57495
fv = 10.53365
fv = 156.9927
fv = 1.340781e+154
fv = 1.340781e+154
fv = 12.21765
fv = 13.11294
fv = 7.766438
fv = 21.59875
fv = 16.10518
fv = 8.644426
fv = 7.766438
fv = 7.766438
fv = 7.503545
fv = 9.157564
fv = 1.340781e+154
fv = 42.7535
fv = 12.21765
fv = 9.098975
fv = 9.157564
fv = 1.340781e+154
fv = 1.340781e+154
fv = 53.68567
fv = 287.5068
fv = 7.503545
fv = 8.423891
fv = 1.340781e+154
fv = 9.085898
fv = 9.085898
fv = 9.085898
fv = 9.085898
fv = 9.085898
fv = 1.340781e+154
fv = 9.955385
fv = 9.085898
fv = 14.40991
fv = 8.353353
fv = 1.340781e+154
fv = 1.340781e+154
fv = 42.7535
fv = 7.503545
fv = 7.881038
fv = 21.98563
fv = 12.21765
fv = 9.240631
fv = 8.644426
fv = 9.406078
fv = 37.33198
fv = 7.503545
fv = 15.15546
fv = 7.503545
fv = 149.6671
fv = 11.79286
fv = 1.340781e+154
fv = 7.766438
fv = 12.21765
fv = 1.340781e+154
fv = 7.766438
fv = 12.5472
fv = 44.91778
fv = 8.386206
fv = 8.849243
fv = 7.503545
fv = 7.503545
fv = 9.735804
fv = 8.678451
fv = 7.605166
fv = 8.200603
fv = 8.4787
fv = 9.422956
fv = 7.503545
fv = 7.503545
fv = 7.503545
fv = 7.712971
fv = 7.503545
fv = 7.503545
fv = 9.801735
fv = 12.21765
run # 3: f=7.50354495
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 81.02425
fv = 42.7535
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.564634
fv = 226836.4
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 354.3647
fv = 410.759
fv = 169.586
fv = 1.340781e+154
fv = 18821946
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.564634
fv = 5.564634
fv = 1.340781e+154
fv = 540.9104
fv = 2554.115
fv = 1.340781e+154
fv = 1.340781e+154
fv = 25.31227
fv = 41373115253
fv = 14.29167
fv = 8.364921
fv = 8.353353
fv = 1.340781e+154
fv = 6.034078
fv = 1.340781e+154
fv = 1.340781e+154
fv = 2777.958
fv = 1.340781e+154
fv = 1226981024
fv = 1725.218
fv = 1609.73
fv = 1750.828
fv = 5.564634
fv = 362.968
fv = 11619.83
fv = 62.9672
fv = 985118997
fv = 5.827507
fv = 481348256012
fv = 5.564634
fv = 5.564634
fv = 5.564634
fv = 1.340781e+154
fv = 1.340781e+154
fv = 19.36189
fv = 668.4565
fv = 5.564634
fv = 1.340781e+154
fv = 34.82834
fv = 1.340781e+154
fv = 8.06437
fv = 1209.159
fv = 958.827
fv = 87237.9
fv = 5.564634
fv = 5.564634
fv = 5.564634
fv = 5.564634
fv = 5.564634
fv = 11721.26
fv = 24.92646
fv = 5.564634
fv = 6.862849
fv = 7.087485
fv = 12.10972
fv = 6.308864
fv = 1.340781e+154
fv = 13.47488
fv = 5.564634
fv = 11.86119
fv = 5.564634
fv = 5.827507
fv = 5.176586
fv = 31.89563
fv = 1.340781e+154
fv = 5.564634
fv = 5.176586
fv = 6.469287
fv = 5.140218
fv = 1006.017
fv = 1.340781e+154
fv = 5.564634
fv = 4.314804
fv = 1.340781e+154
fv = 6.174902
fv = 4.314804
fv = 5.595877
fv = 6.483627
fv = 6.852731
fv = 6.483627
fv = 1.340781e+154
fv = 4.314804
fv = 4.314804
fv = 4.314804
fv = 5.381219
fv = 4.474979
fv = 4.394242
fv = 6.1923
fv = 6.601607
fv = 1.340781e+154
fv = 5.595877
fv = 5.595877
fv = 5.595877
fv = 5.595877
fv = 3098359457
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.957135
fv = 912709.3
fv = 4.314804
fv = 6.053477
fv = 5.126889
fv = 4.314804
fv = 1.340781e+154
fv = 4.837343
fv = 6.483627
fv = 6.483627
fv = 6.130403
fv = 5.564634
fv = 6.174902
fv = 25.83078
fv = 4.314804
fv = 6.483627
fv = 4.874824
fv = 1313.454
fv = 5.301407
fv = 1.340781e+154
fv = 1.340781e+154
fv = 15.33835
fv = 11.73938
fv = 9.326421
fv = 6.469727
fv = 5.574673
fv = 4.314804
fv = 4.314804
fv = 4.314804
fv = 4.314804
fv = 4.314804
fv = 1.340781e+154
fv = 4.314804
fv = 4.314804
fv = 4.314804
fv = 5.564634
fv = 1.340781e+154
fv = 7.861635
fv = 4.314804
run # 4: f=4.314803574
#>
#> bb eval : 930
#> best : 4.002540974
#> worst : 7.50354495
#> solution: x = ( 0 0 1 5 0 0 0 0 1 0 1 1 1 2 1 1 1 2 1 4 ) f(x) = 4.002540974
#>
#>
fv = 4.002541
#> 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
#>
# Score the predictions
predictions$score()
#> regr.mse
#> 6.305913