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 1 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 4 0 5 1 0 8 8 0 6 3 2 6 2 9 9 8 2 9 9 )
#> starting point # 4: ( 0 2 0 0 6 1 4 5 0 9 7 9 4 3 6 7 5 9 4 3 )
#>
#>
fv = 20.90729
fv = 18.09446
fv = 1.340781e+154
fv = 16.30533
fv = 76.16633
fv = 1.340781e+154
fv = 1.340781e+154
fv = 24.0052
fv = 16.32823
fv = 1.340781e+154
fv = 1.340781e+154
fv = 24.0052
fv = 24.32627
fv = 33.01283
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 874.8409
fv = 24.78863
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 16.30533
fv = 12756.47
fv = 1.340781e+154
fv = 1.340781e+154
fv = 16.30533
fv = 32854.98
fv = 577116.7
fv = 1.340781e+154
fv = 1.340781e+154
fv = 2753.968
fv = 16.30533
fv = 16.30533
fv = 1.340781e+154
fv = 21.79294
fv = 1.340781e+154
fv = 2133.334
fv = 24.0052
fv = 60.30195
fv = 24.0052
fv = 985.4236
fv = 20.0182
fv = 3703.567
fv = 1.340781e+154
fv = 15.01276
fv = 1.340781e+154
fv = 1.340781e+154
fv = 52061.98
fv = 1.340781e+154
fv = 1.340781e+154
fv = 16.15128
fv = 16.15128
fv = 163612
fv = 15.01276
fv = 15.42115
fv = 1.340781e+154
fv = 15.42115
fv = 10.99203
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.17323
fv = 5.561046
fv = 1.340781e+154
fv = 1.340781e+154
fv = 13.0779
fv = 1.340781e+154
fv = 2527.143
fv = 1.340781e+154
fv = 2414271
fv = 18.94024
fv = 1.340781e+154
fv = 5.485805
fv = 28260940
fv = 1.340781e+154
fv = 1.340781e+154
fv = 18.55297
fv = 14.61429
fv = 1.340781e+154
fv = 5.485805
fv = 41.05402
fv = 18.86129
fv = 298.8424
fv = 18.86129
fv = 6.925033
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1341.165
fv = 7.872809
fv = 6.925033
fv = 13.10344
fv = 4120.016
fv = 1.340781e+154
fv = 1.340781e+154
fv = 82.92249
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 92.92972
fv = 1.340781e+154
fv = 36.84337
fv = 18.1053
fv = 6.062832
fv = 12.17094
fv = 17.04631
fv = 5.485805
fv = 7.160108
fv = 10.58011
fv = 9.817604
fv = 7.695483
fv = 11.62328
fv = 1.340781e+154
fv = 12.49765
fv = 5.485805
fv = 1.340781e+154
fv = 5.485805
fv = 7.939775
fv = 6.062832
fv = 1.340781e+154
fv = 5.485805
fv = 10.55756
fv = 9.257296
fv = 1.340781e+154
fv = 1.340781e+154
fv = 14.61429
fv = 5.485805
fv = 38054.76
fv = 20.32953
fv = 25.01711
fv = 1.340781e+154
fv = 28260940
fv = 1.340781e+154
fv = 39.31493
fv = 18.86129
fv = 5.485805
fv = 7.615929
fv = 14.61429
fv = 47.36177
fv = 5.485805
fv = 5.485805
fv = 1.340781e+154
fv = 1.340781e+154
fv = 18.86129
fv = 5.485805
fv = 5.485805
fv = 5.620666
fv = 34.73606
fv = 13.10344
fv = 9.257296
fv = 31.98822
fv = 6.925033
fv = 35.58631
fv = 1.340781e+154
fv = 21.4955
fv = 1.340781e+154
fv = 5.485805
fv = 5.485805
fv = 5.485805
fv = 9.942037
fv = 5.485805
fv = 267.1351
fv = 8.432656
fv = 5.485805
fv = 5.858342
fv = 408.3045
fv = 5.620666
fv = 7.785201
fv = 1181.3
fv = 10475825
fv = 1.340781e+154
fv = 14.61429
fv = 171.5292
fv = 5.485805
fv = 16.34446
fv = 5.485805
fv = 5.485805
fv = 10.58011
fv = 5.485805
fv = 35.12365
fv = 15.31543
run # 0: f=5.485805387
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.720655
fv = 39.1685
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.720655
fv = 39.1685
fv = 5.699659
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.554806
fv = 206.4736
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.90653
fv = 1.340781e+154
fv = 9.363448e+13
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.915224
fv = 1.340781e+154
fv = 5.986397
fv = 1.340781e+154
fv = 1.340781e+154
fv = 10.21667
fv = 1.340781e+154
fv = 2269958
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.021531
fv = 5.554806
fv = 5.554806
fv = 14.82256
fv = 1.340781e+154
fv = 10.99613
fv = 339111.4
fv = 5.554806
fv = 1.340781e+154
fv = 5.554806
fv = 1.340781e+154
fv = 1512.178
fv = 234.8295
fv = 5.277424
fv = 5.277424
fv = 1.340781e+154
fv = 39.1685
fv = 16.94341
fv = 1.340781e+154
fv = 1.340781e+154
fv = 12.09712
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.712168
fv = 1.340781e+154
fv = 13.44956
fv = 7.169524
fv = 5.277424
fv = 5.277424
fv = 5.829668
fv = 5.277424
fv = 5.277424
fv = 1.340781e+154
fv = 5.720655
fv = 6.690217
fv = 1.340781e+154
fv = 5.009511
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.13845
fv = 7.412953
fv = 9.996345
fv = 7.809785
fv = 11.49223
fv = 9.169037
fv = 7.412953
fv = 5.009511
fv = 8.396639
fv = 5.134308
fv = 1.340781e+154
fv = 15.21557
fv = 5.009511
fv = 7.027108
fv = 1.340781e+154
fv = 55.52773
fv = 5.009511
fv = 4.543277
fv = 4.543277
fv = 1.340781e+154
fv = 32.48789
fv = 1.340781e+154
fv = 1.340781e+154
fv = 4.543277
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.092341
fv = 4.543277
fv = 1.340781e+154
fv = 1.340781e+154
fv = 50.55221
fv = 4.543277
fv = 11.7331
fv = 4.543277
fv = 4.543277
fv = 4.543277
fv = 292463659
fv = 719.3036
fv = 1.340781e+154
fv = 11.7331
fv = 4.973813
fv = 4.543277
fv = 1.340781e+154
fv = 4.543277
fv = 1.340781e+154
fv = 4.543277
fv = 4.543277
fv = 1.340781e+154
fv = 1.340781e+154
fv = 4.543277
fv = 5.450666
fv = 4.593338
fv = 5.475648
fv = 4.543277
fv = 1.340781e+154
fv = 33.19697
fv = 4.543277
fv = 5.720655
fv = 1.340781e+154
fv = 5.387978
fv = 1.340781e+154
fv = 4.543277
fv = 4.543277
fv = 4.543277
fv = 1.340781e+154
fv = 4.543277
fv = 5.593714
fv = 4.543277
fv = 1.340781e+154
fv = 5.720655
fv = 1.340781e+154
fv = 4.406594
fv = 83.66011
fv = 7095.614
fv = 4.543277
fv = 4.892663
fv = 4.406594
fv = 4.406594
fv = 1.340781e+154
fv = 4.406594
fv = 4.406594
fv = 4.406594
fv = 5.50176
fv = 4.406594
fv = 1.340781e+154
fv = 4.543277
fv = 1.340781e+154
fv = 4.406594
fv = 4.406594
fv = 8.903828
fv = 4.406594
fv = 4.406594
fv = 1.340781e+154
fv = 4.406594
fv = 3.853743
fv = 17525378
fv = 1.340781e+154
fv = 39.1685
fv = 11.7331
fv = 3.853743
fv = 3.853743
fv = 3.853743
fv = 56.71999
fv = 6.057945
fv = 3.853743
fv = 3.853743
fv = 3.853743
fv = 3.888178
fv = 3.853743
fv = 3.853743
fv = 1.340781e+154
fv = 3.853743
fv = 3.853743
fv = 6.763608
fv = 3.853743
fv = 3.853743
fv = 11.74382
fv = 3.853743
fv = 1.340781e+154
fv = 3.853743
fv = 11.82689
fv = 3.853743
fv = 4.563775
fv = 3.853743
fv = 20.06705
fv = 1.340781e+154
fv = 3.853743
fv = 1679768
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 10.58405
fv = 3.853743
fv = 3.853743
fv = 3.853743
fv = 4.181261
fv = 3.853743
fv = 1.340781e+154
fv = 3.853743
fv = 3.853743
fv = 3.853743
fv = 1.340781e+154
fv = 6.057945
fv = 1.340781e+154
fv = 1.340781e+154
fv = 3.853743
fv = 3.853743
fv = 3.853743
fv = 10.58405
fv = 3.853743
fv = 5.475648
fv = 3.853743
fv = 3.888178
fv = 3.853743
fv = 5.105381
fv = 8.008293
fv = 14.17232
fv = 3.853743
fv = 11.74382
fv = 6.057945
fv = 83.66011
fv = 1.340781e+154
fv = 8.689283
fv = 3.853743
fv = 3.853743
fv = 1.340781e+154
fv = 1.340781e+154
fv = 3.853743
fv = 3.888178
fv = 10.58405
fv = 83.66011
fv = 6.265312
run # 1: f=3.853743043
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 12.0888
fv = 39.1685
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.720655
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 36.66023
fv = 1.340781e+154
fv = 17609.2
fv = 6.410962
fv = 3594.212
fv = 812849957
fv = 1.340781e+154
fv = 1.340781e+154
fv = 2042.829
fv = 5.720655
fv = 11.56374
fv = 1.340781e+154
fv = 20.74297
fv = 14193.4
fv = 1.340781e+154
fv = 5.720655
fv = 5.720655
fv = 5.720655
fv = 8.021599
fv = 1.340781e+154
fv = 9.235716
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.636567
fv = 1.340781e+154
fv = 7.479018
fv = 32.49702
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9.541152
fv = 6.572984
fv = 4.94552
fv = 6.809764
fv = 1.340781e+154
fv = 11.06397
fv = 1.340781e+154
fv = 4.30296
fv = 4.30296
fv = 1.340781e+154
fv = 6.775115
fv = 84.11148
fv = 29.10658
fv = 5.720655
fv = 1.340781e+154
fv = 1.340781e+154
fv = 4.30296
fv = 1.340781e+154
fv = 10.57597
fv = 10.57597
fv = 11.01418
fv = 10.57597
fv = 5228.323
fv = 10.57597
fv = 4.30296
fv = 180.7749
fv = 4.739628
fv = 4.30296
fv = 10.57597
fv = 10.57597
fv = 39580.57
fv = 1.340781e+154
fv = 13.77783
fv = 39.1685
fv = 11.7331
fv = 4.30296
fv = 4.30296
fv = 1.340781e+154
fv = 1.340781e+154
fv = 564.4135
fv = 4.648608
fv = 9.814752
fv = 46.88492
fv = 4.30296
fv = 4.30296
fv = 4.30296
fv = 13.028
fv = 17894710
fv = 109.2374
fv = 7.003329
fv = 13.028
fv = 8793.375
fv = 13.028
fv = 2.54343
fv = 1.340781e+154
fv = 39.1685
fv = 9.60669
fv = 23.6282
fv = 1.577872
fv = 4.94552
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.736065
fv = 5.736065
fv = 4.461577
fv = 4.015284
fv = 5.736065
fv = 2.062674
fv = 5.736065
fv = 23.34444
fv = 1.577872
fv = 5.736065
fv = 2.851705
fv = 5.736065
fv = 5.736065
fv = 49.31513
fv = 1.340781e+154
fv = 84.11148
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 39.1685
fv = 10.37679
fv = 1.577872
fv = 1.577872
fv = 1.340781e+154
fv = 1.577872
fv = 1.340781e+154
fv = 1.577872
fv = 1.340781e+154
fv = 2.039027
fv = 13.16542
fv = 3.200239
fv = 2.330128
fv = 2.003328
fv = 2.131118
fv = 1.521314
fv = 446.2409
fv = 1.340781e+154
fv = 41.16719
fv = 5.666299
fv = 1.340781e+154
fv = 1.521314
fv = 103.3408
fv = 1.521314
fv = 1.521314
fv = 2.467734
fv = 1.521314
fv = 1.521314
fv = 1.521314
fv = 2.033523
fv = 2.766878
fv = 1.521314
fv = 1.340781e+154
fv = 1.340781e+154
fv = 2.94958
fv = 2.554355
fv = 1.521314
fv = 1.521314
fv = 1.979788
fv = 1.979788
fv = 1.521314
fv = 1.521314
fv = 1.521314
fv = 4.071578
fv = 2.778841
fv = 6.906245
fv = 2.03039
fv = 1.521314
fv = 1.340781e+154
fv = 49.31513
fv = 1.885429
fv = 1.72551
fv = 19.40084
fv = 1.521314
fv = 1.521314
fv = 1.521314
fv = 1.378589
fv = 6.213761
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 2.333611
fv = 1.378589
fv = 1.378589
fv = 1.378589
fv = 1.588301
fv = 1.340781e+154
fv = 1.340781e+154
fv = 4.721705
fv = 1.340781e+154
fv = 1.378589
fv = 1.378589
fv = 6.365859
fv = 1.49689
fv = 1.378589
fv = 1.480744
fv = 1.521314
fv = 1.340781e+154
fv = 1.378589
fv = 3.00835
fv = 2.467734
fv = 1.378589
fv = 1.378589
fv = 5.695109
fv = 1.378589
fv = 1.585705
fv = 1.340781e+154
fv = 1.378589
fv = 1.378589
fv = 1.599615
fv = 2.50698
run # 2: f=1.378588654
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 304509.8
fv = 39.1685
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.4828e+10
fv = 39.1685
fv = 39.1685
fv = 28.56066
fv = 39.90428
fv = 1.340781e+154
fv = 1.340781e+154
fv = 4.48242
fv = 1.340781e+154
fv = 31104.58
fv = 7.504661
fv = 3039.742
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 34.1782
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 33.46781
fv = 1.340781e+154
fv = 7.504661
fv = 1.340781e+154
fv = 1.340781e+154
fv = 10.13566
fv = 1.340781e+154
fv = 4.48242
fv = 4735.703
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.504661
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5467186
fv = 1.340781e+154
fv = 1.340781e+154
fv = 4.307558
fv = 8.869816
fv = 1.340781e+154
fv = 1.340781e+154
fv = 637.4252
fv = 1.340781e+154
fv = 9.421707
fv = 8.963959
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 25509104
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.503022
fv = 222.0726
fv = 6.238068
fv = 19.8463
fv = 1.340781e+154
fv = 1.340781e+154
fv = 183452.6
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.7792
fv = 8.869816
fv = 1.340781e+154
fv = 4.307558
fv = 1.340781e+154
fv = 3.018777
fv = 1.340781e+154
fv = 11.71188
fv = 1.340781e+154
fv = 1.340781e+154
fv = 128.0598
fv = 3.018777
fv = 11.72532
fv = 1.340781e+154
fv = 15.33872
fv = 15.42436
fv = 15.42436
fv = 16.70355
fv = 15.42436
fv = 3.018777
fv = 13.35498
fv = 16.70355
fv = 4.671076
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 50.10562
fv = 6.758897
fv = 3.726707
fv = 1.340781e+154
fv = 1.340781e+154
fv = 3.726707
fv = 6.059518
fv = 635.2771
fv = 6.805096
fv = 324.6133
fv = 1.340781e+154
fv = 5.754098
fv = 3.018777
fv = 3.018777
fv = 18.14554
fv = 18.14554
fv = 21.29557
fv = 18.14554
fv = 1.340781e+154
fv = 1.340781e+154
fv = 25509104
fv = 7.65648
fv = 129.0347
fv = 50.10562
fv = 17.48145
fv = 5.470665
fv = 5.733032
fv = 3.726707
fv = 3.726707
fv = 7.100841
fv = 6.102913
fv = 9.04538
fv = 1.340781e+154
fv = 1.340781e+154
fv = 3.018777
fv = 1.340781e+154
fv = 2.480703
fv = 4.917807
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.349901
fv = 2.480703
fv = 2.480703
fv = 0.7696037
fv = 5108.081
fv = 1.340781e+154
fv = 33.65359
fv = 5.127112
fv = 9.231105
fv = 1.340781e+154
fv = 1.340781e+154
fv = 0.7254983
fv = 1.340781e+154
fv = 1.340781e+154
fv = 0.7254983
fv = 71.56198
fv = 19.70387
fv = 19.70387
fv = 42.31932
fv = 4.819995
fv = 0.7254983
fv = 15.30518
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 61.11291
fv = 1.340781e+154
fv = 5.275812
fv = 18.97131
fv = 50.79633
fv = 12.54307
fv = 1.340781e+154
fv = 7.334084
fv = 1.340781e+154
fv = 1.340781e+154
fv = 63.64578
fv = 0.7254983
fv = 0.7254983
fv = 41.31425
fv = 158.4028
fv = 16.82227
fv = 0.7254983
fv = 0.7254983
fv = 1.340781e+154
fv = 0.7254983
fv = 0.7254983
fv = 14.2962
fv = 0.7254983
fv = 4.409454
fv = 1.340781e+154
fv = 65.45059
fv = 15.513
fv = 137.577
fv = 4.819995
run # 3: f=0.7254983278
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
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 : 938
#> best : 0.7254983278
#> worst : 1.340780793e+154
#> solution: x = ( 1 0 0 2 0 2 1 5 0 5 1 2 7 2 1 1 1 2 5 1 ) f(x) = 0.7254983278
#>
#>
fv = 0.7254983
#> Warning: optimal degree equals search maximum (1): rerun with larger degree.max optimal degree equals search maximum (5): 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
#>
# Score the predictions
predictions$score()
#> regr.mse
#> 382.3505