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 = 14.7439
fv = 25.44859
fv = 11.63517
fv = 1.291616e+12
fv = 1.340781e+154
fv = 25.23123
fv = 36.05022
fv = 281529.1
fv = 1.340781e+154
fv = 32.7361
fv = 64.66227
fv = 1.340781e+154
fv = 195.5528
fv = 1.340781e+154
fv = 30.02774
fv = 1.340781e+154
fv = 36.05022
fv = 36.05022
fv = 1.340781e+154
fv = 375.5211
fv = 1.340781e+154
fv = 43.39414
fv = 1280746942
fv = 1887.691
fv = 11.63517
fv = 191909.8
fv = 12.31771
#> Warning: number of rows of result is not a multiple of vector length (arg 2)
#>
fv = 1.340781e+154
fv = 28.6005
fv = 14.3167
fv = 14.3167
fv = 517.6986
fv = 1.340781e+154
fv = 13.34183
fv = 1.340781e+154
fv = 44.89909
fv = 10.86952
fv = 115.3481
fv = 1.340781e+154
fv = 10.86952
fv = 10.86952
fv = 8.295215
fv = 866880057329
fv = 1.340781e+154
fv = 1810255
fv = 1.340781e+154
fv = 28.52759
fv = 2.950227e+12
fv = 9.620479
fv = 12.53946
fv = 8.295215
fv = 7.617097
fv = 4.753886
fv = 1.340781e+154
fv = 1.340781e+154
fv = 116864670694
fv = 3.104755e+12
fv = 9.606652
fv = 30.21235
fv = 4.753886
fv = 1.340781e+154
fv = 1.340781e+154
fv = 12.80821
fv = 1.340781e+154
fv = 4.80293
fv = 4.753886
fv = 6.265366
fv = 4.910309
fv = 1.340781e+154
fv = 10.83185
fv = 1.340781e+154
fv = 14.17208
fv = 8977.099
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 13.54169
fv = 1326.83
fv = 12.98782
fv = 10.21281
fv = 6.552927
fv = 4.753886
fv = 9.713638
fv = 10.90073
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.617097
fv = 10.91356
fv = 121.3448
fv = 6.337896
fv = 3.110393e+12
fv = 3.110393e+12
fv = 3.110393e+12
fv = 42.94901
fv = 467566.8
fv = 14.35644
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.469143
fv = 4.753886
fv = 4.52077
fv = 1.340781e+154
fv = 1.340781e+154
fv = 4.249389
fv = 5.916744
fv = 1.340781e+154
fv = 1.340781e+154
fv = 4.249389
fv = 3.937259
fv = 8.16305
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.386355
fv = 6.071408
fv = 43.92541
fv = 3.937259
fv = 4.530766
fv = 4.742535
fv = 13.15501
fv = 3.937259
fv = 3.937259
fv = 3.576303
fv = 766.0209
fv = 1.340781e+154
fv = 84.4957
fv = 1.340781e+154
fv = 4.545301
fv = 3.576303
fv = 3.576303
fv = 4.751371
fv = 6.243674
fv = 35.14927
fv = 6.946279
fv = 4.431455
fv = 19.86111
fv = 4.712546
fv = 4.712546
fv = 6.330636
fv = 4.712546
fv = 23.42143
fv = 3.574598
fv = 7.684972
fv = 1.340781e+154
fv = 10.95998
fv = 12.9764
fv = 6.946279
fv = 4.373109
fv = 8.061773
fv = 8.061773
fv = 4.568585
fv = 4.104544
fv = 3.576303
fv = 6.422971
fv = 19.86111
fv = 7.002354
fv = 4.280358
fv = 4.351369
fv = 14.53371
fv = 4.373109
fv = 3.576303
fv = 8978.812
fv = 6.504158
fv = 3.576303
fv = 39.87361
fv = 8.435655
fv = 9.55816
fv = 1.340781e+154
fv = 35.14927
fv = 5.289445
fv = 58.12768
fv = 157.391
fv = 8.435655
fv = 6.223107
fv = 4.909823
fv = 1.340781e+154
fv = 1.340781e+154
fv = 4.260739
fv = 6.845281
fv = 3.574598
fv = 9.344095
fv = 3.574598
fv = 1.340781e+154
fv = 3.574598
fv = 3.574598
fv = 4.705443
fv = 4.280358
fv = 4.335679
fv = 8.54504
fv = 15.06884
fv = 10.18589
fv = 12.67956
fv = 37.31009
fv = 1.340781e+154
fv = 4.935912
fv = 39.58657
run # 0: f=3.574597707
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 10.24375
fv = 51.4169
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9.971225
fv = 765.2704
fv = 1.340781e+154
fv = 1.340781e+154
fv = 14.59788
fv = 11.09572
fv = 9.971225
fv = 6.526626
fv = 1.340781e+154
fv = 9.49071
fv = 1.340781e+154
fv = 23246.66
fv = 8.101246e+12
#> Warning: number of rows of result is not a multiple of vector length (arg 2)
#>
fv = 7.946913
fv = 9.417362
fv = 9.39214
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.876742
fv = 10.02501
fv = 7.876742
fv = 7.876742
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5985860870
fv = 1006293
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.896401
fv = 6.526626
fv = 6.896401
fv = 31.29389
fv = 9.403135
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.505648
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 10275.63
fv = 83935.49
fv = 164.8795
fv = 1.340781e+154
fv = 6.526626
fv = 1.340781e+154
fv = 25.35214
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 6.526626
fv = 10.1512
fv = 26.22232
fv = 6.896401
fv = 9.122711
fv = 11.1355
fv = 8.824291
fv = 1.340781e+154
#> Warning: number of rows of result is not a multiple of vector length (arg 2)
#>
fv = 8.098377
fv = 1.340781e+154
fv = 6.526626
fv = 12.51519
fv = 6.526626
fv = 6.526626
fv = 6.896401
fv = 1.340781e+154
fv = 1.340781e+154
fv = 4.453817
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.528963
fv = 1.340781e+154
fv = 6.579099
fv = 10.41075
fv = 1.340781e+154
fv = 3.936774
fv = 25.72428
fv = 1.340781e+154
fv = 4.411267
fv = 1.340781e+154
fv = 6.526626
fv = 8.838235
fv = 1.340781e+154
fv = 1.340781e+154
fv = 3.936774
fv = 3.936774
fv = 67.43748
fv = 3.536201
fv = 109.6042
fv = 1.340781e+154
fv = 2.978642
fv = 15.04114
fv = 1.340781e+154
fv = 13.69293
fv = 17.69929
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 11.91384
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 685312304
fv = 2.978642
fv = 4.385275
fv = 1.340781e+154
fv = 1.67674
fv = 69180.07
fv = 1.340781e+154
fv = 4.815486
fv = 1.340781e+154
fv = 3.60013
fv = 3.60013
fv = 3.60013
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.652972
fv = 2.704994
fv = 1.67674
fv = 14.2491
fv = 18.53968
fv = 23.1936
fv = 619.2096
fv = 351.635
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9.2381
fv = 3.052867
fv = 20.15781
fv = 8.93693
fv = 1.67674
fv = 940732723285
fv = 2.466889
fv = 1.340781e+154
fv = 25.19886
fv = 14.26852
fv = 163.4216
fv = 1.340781e+154
fv = 136.6757
fv = 2055.254
fv = 64747264
fv = 2055.254
fv = 3829.864
fv = 3829.864
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9.482438
fv = 12.79448
fv = 12.79448
fv = 21.54468
fv = 12.56351
fv = 144572.3
fv = 8.553305
fv = 17.57684
fv = 21.24836
fv = 14.18535
fv = 13.17527
fv = 1.340781e+154
fv = 1.67674
fv = 1.340781e+154
fv = 1.67674
fv = 4.515331
fv = 148.2626
fv = 6.468462
fv = 21.72262
fv = 1.340781e+154
fv = 1.340781e+154
fv = 3.823439
fv = 1.340781e+154
fv = 8.686483
fv = 4215.196
fv = 18681.85
fv = 2.791435
fv = 644.0075
fv = 1.340781e+154
fv = 5.151225
fv = 1.67674
fv = 1.67674
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.67674
fv = 1.965747
fv = 14.18535
fv = 785.8543
fv = 6.554622
fv = 52.30054
fv = 51.4169
run # 1: f=1.67673957
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.051852
fv = 51.4169
fv = 1.340781e+154
fv = 1.340781e+154
fv = 22.94334
fv = 8.051852
fv = 15.71513
fv = 29.48458
fv = 22.01786
fv = 14.34598
fv = 68.6593
fv = 15.71513
fv = 8.169242
fv = 10.06671
fv = 8.051852
fv = 1.340781e+154
fv = 15.71513
fv = 8398.285
fv = 9.72027
fv = 1.340781e+154
fv = 1.340781e+154
fv = 392043.1
fv = 8.491371
fv = 1.340781e+154
fv = 1.340781e+154
fv = 39.58657
fv = 12.82493
fv = 11.25953
fv = 1.340781e+154
fv = 8.90932
fv = 1.340781e+154
fv = 23.20367
fv = 58708.47
fv = 249.6581
fv = 52388.52
fv = 23.20367
fv = 223.3352
fv = 8.051852
fv = 9.383607
fv = 8.051852
fv = 23.20367
fv = 102473.2
fv = 23.20367
fv = 1.340781e+154
fv = 23.20367
fv = 1.340781e+154
fv = 4.43431
fv = 13.4588
fv = 1.340781e+154
fv = 10.88848
fv = 7747642644
fv = 9.861002
fv = 4.43431
fv = 1.340781e+154
fv = 4.43431
fv = 4.916699
fv = 3.58614
fv = 838.5186
fv = 1.340781e+154
fv = 14.51776
fv = 5.70619
fv = 6.956705
fv = 5.70619
fv = 7.700229
fv = 1.340781e+154
fv = 4.931116
fv = 3.58614
fv = 4.52187
fv = 4.36945
fv = 6.522633
fv = 68237.66
fv = 1.340781e+154
fv = 9.397889
fv = 1.340781e+154
fv = 1.340781e+154
fv = 13.46681
fv = 1.340781e+154
fv = 9.203396
fv = 1688825
fv = 51.07053
fv = 10.40558
fv = 1.340781e+154
fv = 40.78136
fv = 14.88669
fv = 1.340781e+154
fv = 3.804183
fv = 14781.76
fv = 6.83422
fv = 6.40304
fv = 5.566467
fv = 28.81886
fv = 5.566467
fv = 5.639861
fv = 1.340781e+154
fv = 3.743066
fv = 3.58614
fv = 3.58614
fv = 2153.479
fv = 6.59798
fv = 7.006749
fv = 5.111262
fv = 5.859465
fv = 5.894323
fv = 17.30963
fv = 3.960886
fv = 1.340781e+154
fv = 34.67501
fv = 3.58614
fv = 3.924552
fv = 3.804183
fv = 5.64304
fv = 3.58614
fv = 3.924552
fv = 3.58614
fv = 3.743066
fv = 3.913843
fv = 5.779347
fv = 5.487325
fv = 3.83303
fv = 4.227707
fv = 3.58614
fv = 3.58614
fv = 4.101432
fv = 3.804183
fv = 4.157791
fv = 3.58614
fv = 8.113911
run # 2: f=3.586139917
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 563.1931
fv = 51.4169
fv = 34.67501
fv = 13.04061
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 3979.968
fv = 10.43319
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 12.12906
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 218536090902
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 167.673
fv = 11.50008
fv = 11.35676
fv = 11.35676
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 8.40936
fv = 10.35427
fv = 1.340781e+154
fv = 1.340781e+154
fv = 38.43777
fv = 201.0096
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.78701
fv = 158740040
fv = 6.800467
fv = 6.800467
fv = 1.340781e+154
fv = 6.800467
fv = 6.800467
fv = 1.340781e+154
fv = 1.340781e+154
fv = 7.894158e+12
fv = 1.340781e+154
fv = 51.6482
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.074916e+13
fv = 4.980432
fv = 51.4169
fv = 1.340781e+154
fv = 12.64793
fv = 1.377684e+14
fv = 4.980432
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.377684e+14
fv = 7.051807
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 115034.9
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 17.77941
fv = 1.340781e+154
fv = 10.77871
fv = 51.4169
fv = 4.704247
fv = 13.4588
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 51.4169
fv = 4.704247
fv = 91279231
fv = 4.704247
fv = 4.704247
fv = 4.704247
fv = 691964268596
fv = 92673160
fv = 1.340781e+154
fv = 51.4169
fv = 51.4169
fv = 57593.54
fv = 1.340781e+154
fv = 51.4169
fv = 1.340781e+154
fv = 51.4169
fv = 1.340781e+154
fv = 23.16946
fv = 34.67501
fv = 4.704247
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1564.682
fv = 1.340781e+154
fv = 8.035846
fv = 4.704247
fv = 4.985762
fv = 13054.3
fv = 19.40018
fv = 517118460719
fv = 5.580737
fv = 5.580737
fv = 6.641351
fv = 4.704247
fv = 1.340781e+154
fv = 1157.009
fv = 1.340781e+154
fv = 1164.362
fv = 1.340781e+154
fv = 1.340781e+154
fv = 4.704247
fv = 51.4169
fv = 1.340781e+154
fv = 1.340781e+154
fv = 36.18385
fv = 6.115524
fv = 1.340781e+154
fv = 4.704247
fv = 4.704247
fv = 4.704247
fv = 4.704247
fv = 5.624684
fv = 10.1495
fv = 5.205597
fv = 22.70179
fv = 4.704247
fv = 4.704247
fv = 4.704247
fv = 4.808705
fv = 12.30438
fv = 1.340781e+154
fv = 1035.587
fv = 199.8865
fv = 625.0123
fv = 360.4997
fv = 143988.7
fv = 51.4169
fv = 1.340781e+154
fv = 4.671535
fv = 5.38954
fv = 1.340781e+154
fv = 113.6511
fv = 5.580737
fv = 4.671535
fv = 4.671535
fv = 1.340781e+154
fv = 4.671535
fv = 5.126378
fv = 10.88848
fv = 1.340781e+154
fv = 27.22718
fv = 9.158292
fv = 4.671535
fv = 4.671535
fv = 4.689924
fv = 4.671535
fv = 4.704247
fv = 1.340781e+154
fv = 6.67602
fv = 4.671535
fv = 4.671535
fv = 1.340781e+154
fv = 1.340781e+154
fv = 113.6511
fv = 4.671535
fv = 4.704247
fv = 4.704247
fv = 4.640835
fv = 7.751738
fv = 1.340781e+154
fv = 18.64831
fv = 3.992172
fv = 1.340781e+154
fv = 1.340781e+154
fv = 5.811988
fv = 1.340781e+154
fv = 4.020022
fv = 6.641351
fv = 3.992172
fv = 1.340781e+154
fv = 3.992172
fv = 1.340781e+154
fv = 3.992172
fv = 3.992172
fv = 9.380783
fv = 37.16357
fv = 5.457775
fv = 489.0997
fv = 3.992172
fv = 7.399107
fv = 2.837432
fv = 191340.7
fv = 1.340781e+154
fv = 1.340781e+154
fv = 482.7291
fv = 1.340781e+154
fv = 1.340781e+154
fv = 2.837432
fv = 18.23104
fv = 4.515361
fv = 3.858378
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 2.837432
fv = 2.837432
fv = 2.837432
fv = 2.837432
fv = 3.326913
fv = 12.2833
fv = 10.34612
fv = 170.8338
fv = 35.39703
fv = 35.39703
fv = 1.340781e+154
fv = 1.340781e+154
fv = 9.437546
fv = 4.334051
fv = 1.340781e+154
fv = 4.334051
fv = 2.837432
fv = 2.837432
fv = 1.340781e+154
fv = 2.837432
fv = 9.437546
fv = 1.340781e+154
fv = 13.96172
fv = 9.437546
fv = 1268.203
fv = 1.340781e+154
fv = 1.340781e+154
fv = 10122.74
fv = 5.383128
fv = 14.71582
fv = 1.340781e+154
fv = 9.437546
fv = 2.837432
fv = 2.837432
fv = 3.561636
fv = 2.622943
fv = 102.3065
fv = 32290.62
fv = 16335.03
fv = 1.340781e+154
fv = 9.437546
fv = 1.340781e+154
fv = 2.622943
fv = 2.622943
fv = 2.622943
fv = 6.488082
fv = 2.622943
fv = 2.145544
fv = 321.0192
fv = 17.165
fv = 1.340781e+154
fv = 1.340781e+154
fv = 2.145544
fv = 2.145544
fv = 2.793651
fv = 5.728092
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 2.145544
fv = 1.340781e+154
fv = 4.20374
fv = 2.145544
fv = 2.600559
fv = 3.694216
fv = 2.145544
fv = 2.145544
fv = 16.411
fv = 258154.4
fv = 1.340781e+154
fv = 1.340781e+154
run # 3: f=2.145543647
#>
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
fv = 1.340781e+154
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 : 948
#> best : 1.67673957
#> worst : 1.340780793e+154
#> solution: x = ( 0 0 1 2 0 1 1 0 0 1 1 1 2 5 1 2 2 3 1 2 ) f(x) = 1.67673957
#>
#>
fv = 1.67674 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...
# Score the predictions
predictions$score()
#> regr.mse
#> 15.69344