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 = 11.60935
fv = 10.14146
fv = 1.340781e+154
fv = 21.74581
fv = 1.340781e+154
fv = 44.10038
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 906.0207
fv = 10.14146
fv = 1.340781e+154
fv = 1.340781e+154
fv = 44.10038
fv = 1.340781e+154
fv = 55.77944
fv = 55.77944
fv = 2845.087
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.017526
fv = 11.90118
fv = 1.340781e+154
fv = 9.000513
fv = 66.01913
fv = 7.906127
fv = 11.41702
fv = 600872377678
fv = 2436061452
fv = 1.340781e+154
fv = 7.995491
fv = 7.995491
fv = 8.217584
fv = 25.95687
fv = 7.017526
fv = 2246.209
fv = 286715.4
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 30.21627
fv = 1.340781e+154
fv = 28.05025
fv = 8.833954
fv = 8.510283
fv = 12.42615
fv = 11.51799
fv = 14.89806
fv = 20.14653
fv = 8.833954
fv = 8.205609
fv = 1.340781e+154
fv = 10.16079
fv = 21.56341
fv = 33643.97
fv = 7.017526
fv = 7.906127
fv = 1.340781e+154
fv = 6.885693
fv = 1.340781e+154
#> Warning: number of rows of result is not a multiple of vector length (arg 2)
#>
fv = 1.340781e+154
fv = 10.45777
fv = 1.340781e+154
fv = 16.37331
fv = 6.885693
fv = 6.885693
fv = 6.885693
fv = 6.885693
fv = 6.885693
fv = 7.012905
fv = 1.340781e+154
fv = 7.78902
fv = 9.476995
fv = 7.600161
fv = 8.440583
fv = 9.711385
fv = 7.725595
fv = 28.75308
#> Warning: number of rows of result is not a multiple of vector length (arg 2)
#>
fv = 23.88975
fv = 17.39628
fv = 34720.62
fv = 1.340781e+154
fv = 1.340781e+154
fv = 12.71677
fv = 12.71677
fv = 9.133445
fv = 7.078826
fv = 5.518834
fv = 4503987
fv = 1.340781e+154
fv = 8.853944
fv = 7.541855
fv = 5.518834
fv = 5.66429
fv = 6.12342
fv = 10.03528
fv = 5.957652
fv = 9.793617
fv = 6.637779
fv = 6.637779
fv = 6.616862
fv = 25.76278
fv = 5.250762
fv = 1.011956e+13
fv = 1.340781e+154
fv = 20.36619
fv = 33.36389
fv = 15.36407
fv = 19.05547
fv = 15.36407
fv = 1.340781e+154
fv = 5.404476
fv = 5.250762
fv = 6.853618
fv = 11.5058
fv = 19.36993
fv = 12.10879
fv = 5.250762
fv = 7.369728
fv = 4.585807
fv = 1.340781e+154
fv = 25.54832
fv = 4.585807
fv = 128.7824
fv = 4.585807
fv = 4.649878
fv = 8.147884
fv = 5.398647e+12
fv = 8.171266
fv = 1.57696e+13
fv = 40.30411
fv = 13.88449
fv = 4.388228
fv = 6.257307
fv = 1.340781e+154
fv = 157.4545
fv = 3.003385e+12
fv = 6.064067
fv = 6.576186
fv = 17.48284
fv = 4.388228
fv = 1.340781e+154
fv = 6.576186
fv = 1.340781e+154
fv = 6.936805
fv = 11.7349
fv = 6.576186
fv = 6.733044
fv = 50.56342
fv = 253.4959
fv = 50.56342
fv = 92.36506
fv = 32.70898
fv = 1.340781e+154
fv = 1.340781e+154
fv = 65.9769
fv = 5.267387
fv = 4.162439
fv = 4.573635
fv = 1.340781e+154
fv = 10.45147
fv = 13.12477
fv = 4.162439
fv = 4.83279
fv = 4.162439
fv = 4.508795
fv = 7.017507
fv = 1.340781e+154
fv = 4.573635
fv = 32.86994
fv = 6.076218
fv = 6.126772
fv = 16.39737
fv = 1.340781e+154
fv = 10.82873
fv = 43818805
fv = 19.94915
fv = 1.340781e+154
fv = 5.518763e+12
fv = 164.0119
fv = 15.16414
fv = 37.98582
fv = 4.162439
fv = 1.340781e+154
fv = 4.162439
fv = 106.6888
fv = 10.65875
fv = 8.674352e+12
fv = 4.68467
fv = 5.351073
fv = 6.853314
run # 0: f=4.162439157
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 14581.26
fv = 7.375178
fv = 23.03771
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.375178
fv = 7.375178
fv = 7.375178
fv = 5.742657
fv = 78.24893
fv = 1.340781e+154
fv = 1.340781e+154
fv = 28014951082
fv = 63080.71
fv = 7.783709
fv = 1.340781e+154
fv = 1.340781e+154
fv = 23139624366
fv = 6928.41
fv = 6.598289
fv = 6.598289
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 16.14962
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 28014951082
fv = 12.55792
fv = 8.190174
fv = 15.95817
fv = 15.95817
fv = 1.340781e+154
fv = 18.08993
fv = 15.95817
fv = 1.340781e+154
fv = 6.598289
fv = 8.215972
fv = 5.742657
fv = 1.340781e+154
fv = 7.652547
fv = 11.09544
fv = 5.742657
fv = 7.183715
fv = 5.742657
fv = 1611.607
fv = 1.340781e+154
fv = 1.340781e+154
fv = 22.8254
fv = 13.87717
fv = 1061.746
fv = 5.836599
fv = 7.408472
fv = 7.875557
fv = 5.742657
fv = 5.742657
fv = 10.29
fv = 6.598289
fv = 5.742657
fv = 1.340781e+154
fv = 80.88948
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.036325
fv = 5.036325
fv = 1.340781e+154
fv = 22.8254
fv = 6.564182
fv = 5.335445
fv = 5.036325
fv = 5.036325
fv = 5.583259
fv = 5.036325
fv = 6.084159
fv = 1.340781e+154
fv = 5.606524
fv = 1.340781e+154
fv = 5.877858
fv = 5.036325
fv = 6.434211
fv = 7.889482
fv = 8.606212
fv = 8.606212
fv = 7.126863
fv = 21.25448
fv = 504.6683
fv = 3126.905
fv = 32.88564
fv = 1.340781e+154
fv = 6.598289
fv = 5.036325
fv = 5.036325
fv = 5.841611
fv = 5.457931
fv = 5.036325
fv = 1.340781e+154
fv = 5.036325
fv = 5.68289
fv = 5.583259
fv = 6.824804
fv = 5.877858
fv = 5.606524
fv = 5.335445
fv = 6.212259
fv = 5.864694
fv = 5.036325
fv = 6.598289
fv = 7.157507
fv = 5.613446
fv = 24.47989
fv = 7.889482
fv = 1.340781e+154
fv = 6.153806
fv = 6.35616
fv = 6.873551
fv = 5.036325
fv = 5.457931
fv = 5.93647
fv = 5.93647
fv = 5.93647
fv = 5.036325
fv = 5.036325
fv = 5.036325
fv = 5.77247
fv = 5.036325
fv = 5.335445
fv = 5.036325
fv = 1.340781e+154
fv = 5.606524
fv = 6.564182
fv = 5.877858
fv = 5.036325
fv = 5.036325
fv = 5.036325
fv = 5.864694
fv = 6.598289
fv = 5.036325
fv = 6.824804
fv = 4.535567
fv = 192.8036
fv = 1.340781e+154
fv = 6.153806
fv = 5.77247
fv = 4.535567
fv = 4.535567
fv = 4.535567
fv = 4.535567
fv = 4.535567
fv = 1.340781e+154
fv = 6.078105
fv = 5.112536
fv = 5.275456
fv = 4.790403
fv = 4.346938
fv = 1.340781e+154
fv = 8.333985
fv = 1.340781e+154
fv = 5.099835
fv = 7.227893
fv = 4.346938
fv = 4.346938
fv = 3.975646
fv = 1.340781e+154
fv = 11.68089
fv = 6.895766
fv = 5.756637
fv = 3.975646
fv = 1.340781e+154
fv = 4.163064
fv = 6.096359
fv = 5.05357
fv = 3.975646
fv = 5.776694
fv = 4.521577
fv = 5.829909
fv = 7.256553
fv = 3.975646
fv = 3.975646
fv = 6.946891
fv = 3.413049
fv = 1.340781e+154
fv = 8.305334
fv = 1.340781e+154
fv = 4.584246
fv = 4.114653
fv = 3.413049
fv = 1.340781e+154
fv = 1.340781e+154
fv = 3.413049
fv = 3.906897
fv = 7.222474
fv = 3.787447
fv = 5.979503
fv = 7.222474
fv = 3.413049
fv = 4.833913
fv = 3.608992
fv = 5.398379
fv = 3.413049
fv = 3.413049
fv = 6.347181
fv = 1.340781e+154
fv = 1341.833
run # 1: f=3.413049044
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 56.1738
fv = 26.271
fv = 26.271
fv = 1.340781e+154
fv = 1.340781e+154
fv = 26.271
fv = 26.271
fv = 1.340781e+154
fv = 1720868883
fv = 1.340781e+154
fv = 663236480793
fv = 26.271
fv = 26.271
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.087255
fv = 1239.236
fv = 5.975498
fv = 7.583331
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 119.2934
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 111.4031
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.313099
fv = 483857.9
fv = 1.340781e+154
fv = 6.98009
fv = 5.727396
fv = 7499.499
fv = 1.340781e+154
fv = 7.583331
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 93.75987
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 84.83306
fv = 6.233515
fv = 6.233515
fv = 259224
fv = 6.482303
fv = 343951086
fv = 1.706612
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9672825428
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.535973
fv = 6.110271
fv = 9.130531
fv = 1.340781e+154
fv = 9.347316
fv = 8.442274
fv = 9.347316
fv = 6.392213
fv = 9.347316
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 20.0967
fv = 1.340781e+154
fv = 6.206312
fv = 1.776976
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.776976
fv = 9.219385
fv = 9.219385
fv = 1564.817
fv = 1.340781e+154
fv = 11.12115
fv = 9.116169
fv = 1.340781e+154
fv = 6.392213
fv = 5.580238
fv = 72.6192
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.535973
fv = 1.706612
fv = 2.635372
fv = 2380.218
fv = 1.706612
fv = 23625.72
fv = 196.8799
fv = 1.340781e+154
fv = 1.706612
fv = 1.706612
fv = 1.706612
fv = 1.706612
fv = 1.706612
fv = 3.29255
fv = 1.706612
fv = 1.340781e+154
fv = 91442.77
fv = 1.340781e+154
fv = 1.340781e+154
fv = 35331.52
fv = 1.340781e+154
fv = 1.340781e+154
fv = 484.6898
fv = 8.535973
fv = 2.635372
fv = 1.796617
fv = 1.340781e+154
fv = 1.706612
fv = 1.544373
fv = 28.69091
fv = 1.340781e+154
fv = 1.340781e+154
fv = 23.42457
fv = 1.340781e+154
fv = 6.181498
fv = 1.340781e+154
fv = 1.544373
fv = 3443.448
fv = 2.67674
fv = 6.827877
fv = 1.544373
fv = 1.340781e+154
fv = 6.416017
fv = 6.416017
fv = 2.162485
fv = 2.162485
fv = 1.544373
fv = 8.923602
fv = 2.162485
fv = 7.460891
fv = 1.340781e+154
fv = 137.8662
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 2.597991
fv = 8.974204
fv = 2.094269
fv = 5.500085
fv = 1.544373
fv = 1.508461
fv = 1.340781e+154
fv = 146.817
fv = 8.994709
fv = 6.031039
fv = 1.89799
fv = 1.340781e+154
fv = 1.89799
fv = 1.508461
fv = 1.508461
fv = 2.778932
fv = 1.340781e+154
fv = 5.398334
fv = 1.340781e+154
fv = 2.578619
fv = 1.340781e+154
fv = 1.508461
fv = 9.553478
fv = 2.579821
fv = 1.508461
fv = 1.508461
fv = 2165.282
fv = 1.340781e+154
fv = 42.23156
fv = 23.26724
fv = 8.974204
fv = 3.896488
fv = 1.508461
fv = 2.162485
fv = 1.508461
fv = 1.508461
fv = 1.340781e+154
fv = 1.508461
fv = 5.807224
fv = 15.23234
fv = 6.827877
fv = 8.095257
fv = 2.372313
fv = 1.508461
fv = 1.508461
fv = 5.398334
fv = 1.851622
fv = 6400.878
run # 2: f=1.50846085
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
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 = 7.832444
fv = 26.271
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.586898
fv = 20.17886
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.750141
fv = 6.480134
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 83314.6
fv = 1593416
fv = 4.495767
fv = 1.340781e+154
fv = 1.340781e+154
fv = 22.06866
fv = 1.340781e+154
fv = 1.340781e+154
fv = 280369163
fv = 4.495767
fv = 12.27929
fv = 1.340781e+154
fv = 1.340781e+154
fv = 190269106
fv = 1.340781e+154
fv = 1.340781e+154
fv = 215.0775
fv = 190269106
fv = 1.340781e+154
fv = 10.31006
fv = 1.340781e+154
fv = 248665.5
fv = 1.340781e+154
fv = 1.340781e+154
fv = 4989.918
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.25986
fv = 1.340781e+154
fv = 1.340781e+154
fv = 24.15611
fv = 7.099806
fv = 15.10334
fv = 1593416
fv = 64.88773
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.687823
fv = 305646.7
fv = 4572815
fv = 6.480134
fv = 171.5882
fv = 6.480134
fv = 8.687823
fv = 1.340781e+154
fv = 1.340781e+154
fv = 30064.28
fv = 1.340781e+154
fv = 1.340781e+154
fv = 26.271
fv = 1.340781e+154
fv = 4.495767
fv = 1.340781e+154
fv = 4.495767
fv = 4.495767
fv = 10.6
fv = 11.19256
fv = 61.62725
fv = 10.62802
fv = 9.840258
fv = 1.340781e+154
fv = 51.08229
fv = 61.69308
fv = 1.340781e+154
fv = 34.47019
fv = 452.2246
fv = 30.68086
fv = 11.05345
fv = 11.05345
fv = 4.495767
fv = 1.340781e+154
fv = 1.340781e+154
fv = 26.271
fv = 18.39022
fv = 13.8438
fv = 20.25689
fv = 39.06584
fv = 6.480134
fv = 23.38712
fv = 4.495767
fv = 4.495767
fv = 4.495767
fv = 4.495767
fv = 4.495767
fv = 1.340781e+154
fv = 17.32083
fv = 4.495767
fv = 4.495767
fv = 6.529597
fv = 26.71869
fv = 8.792777
fv = 44.44213
fv = 11.14159
fv = 55.41046
fv = 4.495767
fv = 1.340781e+154
fv = 4.495767
fv = 1.340781e+154
fv = 26.271
fv = 30.68616
fv = 5.724301
fv = 1.340781e+154
fv = 7.47252
run # 4: f=4.495766705
#>
#> bb eval : 837
#> best : 1.50846085
#> worst : 1.340780793e+154
#> solution: x = ( 0 5 0 5 0 0 2 0 1 0 1 1 4 1 2 3 1 1 1 3 ) f(x) = 1.50846085
#>
#>
fv = 1.508461
#> 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
#>
# Score the predictions
predictions$score()
#> regr.mse
#> 19.02415