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Type 'q()' to quit R. > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/tests.html > # * https://testthat.r-lib.org/reference/test_package.html#special-files > > Sys.setenv("OMP_THREAD_LIMIT" = 2) > Sys.setenv("Ncpu" = 2) > > library(testthat) > library(mlexperiments) > > test_check("mlexperiments") CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 CV fold: Fold4 CV fold: Fold5 Testing for identical folds in 2 and 1. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold1 CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 3.28 Round = 1 k = 16.0000 Value = -0.1487759 elapsed = 3.47 Round = 2 k = 64.0000 Value = -0.123666 elapsed = 3.21 Round = 3 k = 10.0000 Value = -0.1638418 elapsed = 3.15 Round = 4 k = 34.0000 Value = -0.1321406 elapsed = 3.17 Round = 5 k = 80.0000 Value = -0.1217828 elapsed = 3.37 Round = 6 k = 50.0000 Value = -0.1246077 Best Parameters Found: Round = 5 k = 80.0000 Value = -0.1217828 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 3.36 Round = 1 k = 16.0000 Value = -0.1487759 elapsed = 3.42 Round = 2 k = 64.0000 Value = -0.123666 elapsed = 3.05 Round = 3 k = 10.0000 Value = -0.1638418 elapsed = 3.19 Round = 4 k = 34.0000 Value = -0.1321406 elapsed = 3.15 Round = 5 k = 80.0000 Value = -0.1217828 elapsed = 3.16 Round = 6 k = 50.0000 Value = -0.1246077 Best Parameters Found: Round = 5 k = 80.0000 Value = -0.1217828 elapsed = 3.34 Round = 1 k = 24.0000 Value = -0.1440678 elapsed = 3.49 Round = 2 k = 63.0000 Value = -0.1233522 elapsed = 3.25 Round = 3 k = 34.0000 Value = -0.1321406 elapsed = 3.30 Round = 4 k = 71.0000 Value = -0.1220967 elapsed = 3.33 Round = 5 k = 2.0000 Value = -0.2743252 elapsed = 3.56 Round = 6 k = 80.0000 Value = -0.1217828 Best Parameters Found: Round = 6 k = 80.0000 Value = -0.1217828 Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 0.94 Round = 1 k = 16.0000 Value = -0.1520798 elapsed = 1.03 Round = 2 k = 64.0000 Value = -0.1313681 elapsed = 1.11 Round = 3 k = 10.0000 Value = -0.1859821 elapsed = 1.05 Round = 4 k = 34.0000 Value = -0.1398453 elapsed = 1.06 Round = 5 k = 65.0000 Value = -0.132307 elapsed = 1.03 Round = 6 k = 52.0000 Value = -0.1290153 Best Parameters Found: Round = 6 k = 52.0000 Value = -0.1290153 CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 1.00 Round = 1 k = 16.0000 Value = -0.1577268 elapsed = 1.05 Round = 2 k = 64.0000 Value = -0.1153539 elapsed = 1.10 Round = 3 k = 10.0000 Value = -0.1732636 elapsed = 1.06 Round = 4 k = 34.0000 Value = -0.1360669 elapsed = 1.13 Round = 5 k = 80.0000 Value = -0.1082897 elapsed = 1.02 Round = 6 k = 51.0000 Value = -0.1243006 Best Parameters Found: Round = 5 k = 80.0000 Value = -0.1082897 CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 0.97 Round = 1 k = 16.0000 Value = -0.1577195 elapsed = 1.04 Round = 2 k = 64.0000 Value = -0.1299503 elapsed = 1.02 Round = 3 k = 10.0000 Value = -0.1798522 elapsed = 1.00 Round = 4 k = 34.0000 Value = -0.1384196 elapsed = 1.11 Round = 5 k = 77.0000 Value = -0.1304132 elapsed = 0.90 Round = 6 k = 50.0000 Value = -0.1341896 Best Parameters Found: Round = 2 k = 64.0000 Value = -0.1299503 CV fold: Fold1 Parameter settings [=======>------------------------------------] 2/11 ( 18%) Parameter settings [===========>--------------------------------] 3/11 ( 27%) Parameter settings [===============>----------------------------] 4/11 ( 36%) Parameter settings [===================>------------------------] 5/11 ( 45%) Parameter settings [=======================>--------------------] 6/11 ( 55%) Parameter settings [===========================>----------------] 7/11 ( 64%) Parameter settings [===============================>------------] 8/11 ( 73%) Parameter settings [===================================>--------] 9/11 ( 82%) Parameter settings [======================================>----] 10/11 ( 91%) Parameter settings [===========================================] 11/11 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=======>------------------------------------] 2/11 ( 18%) Parameter settings [===========>--------------------------------] 3/11 ( 27%) Parameter settings [===============>----------------------------] 4/11 ( 36%) Parameter settings [===================>------------------------] 5/11 ( 45%) Parameter settings [=======================>--------------------] 6/11 ( 55%) Parameter settings [===========================>----------------] 7/11 ( 64%) Parameter settings [===============================>------------] 8/11 ( 73%) Parameter settings [===================================>--------] 9/11 ( 82%) Parameter settings [======================================>----] 10/11 ( 91%) Parameter settings [===========================================] 11/11 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=======>------------------------------------] 2/11 ( 18%) Parameter settings [===========>--------------------------------] 3/11 ( 27%) Parameter settings [===============>----------------------------] 4/11 ( 36%) Parameter settings [===================>------------------------] 5/11 ( 45%) Parameter settings [=======================>--------------------] 6/11 ( 55%) Parameter settings [===========================>----------------] 7/11 ( 64%) Parameter settings [===============================>------------] 8/11 ( 73%) Parameter settings [===================================>--------] 9/11 ( 82%) Parameter settings [======================================>----] 10/11 ( 91%) Parameter settings [===========================================] 11/11 (100%) CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold1 CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Classification: using 'mean misclassification error' as optimization metric. elapsed = 3.38 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485 Classification: using 'mean misclassification error' as optimization metric. elapsed = 3.37 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09196485 Classification: using 'mean misclassification error' as optimization metric. elapsed = 3.48 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09196485 Classification: using 'mean misclassification error' as optimization metric. elapsed = 3.42 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09196485 Classification: using 'mean misclassification error' as optimization metric. elapsed = 3.19 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09196485 Classification: using 'mean misclassification error' as optimization metric. elapsed = 3.00 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09196485 Classification: using 'mean misclassification error' as optimization metric. elapsed = 2.94 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09196485 Classification: using 'mean misclassification error' as optimization metric. elapsed = 3.22 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09196485 Classification: using 'mean misclassification error' as optimization metric. elapsed = 3.34 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09196485 Classification: using 'mean misclassification error' as optimization metric. elapsed = 2.32 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2715003 Classification: using 'mean misclassification error' as optimization metric. elapsed = 3.03 Round = 11 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403 Classification: using 'mean misclassification error' as optimization metric. elapsed = 2.73 Round = 12 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403 Best Parameters Found: Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485 Classification: using 'mean misclassification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean misclassification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean misclassification error' as optimization metric. CV fold: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.30 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.34 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09558117 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.26 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09558117 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.30 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09558117 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.29 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09558117 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.36 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09558117 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.44 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09558117 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.35 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09558117 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.31 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09558117 Classification: using 'mean misclassification error' as optimization metric. elapsed = 0.78 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2853118 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.22 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.09746574 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.42 Round = 12 minsplit = 12.0000 cp = 0.09946662 maxdepth = 30.0000 Value = -0.09558117 Best Parameters Found: Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117 CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.20 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.29 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.08333478 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.31 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.08333478 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.28 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.08333478 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.31 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.08333478 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.22 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.08333478 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.24 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.08333478 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.23 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.08333478 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.18 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.08333478 Classification: using 'mean misclassification error' as optimization metric. elapsed = 0.72 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2806083 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.16 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.08333478 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.28 Round = 12 minsplit = 42.0000 cp = 0.07902683 maxdepth = 30.0000 Value = -0.08333478 Best Parameters Found: Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478 CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.26 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.27 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.1130091 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.26 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.1148897 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.41 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.1130091 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.36 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.1130091 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.28 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.1148897 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.34 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.1130091 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.38 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.1130091 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.26 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.1130091 Classification: using 'mean misclassification error' as optimization metric. elapsed = 0.70 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2796667 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.23 Round = 11 minsplit = 28.0000 cp = 0.04674927 maxdepth = 14.0000 Value = -0.1130091 Classification: using 'mean misclassification error' as optimization metric. elapsed = 1.39 Round = 12 minsplit = 57.0000 cp = 0.06694248 maxdepth = 30.0000 Value = -0.1130091 Best Parameters Found: Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091 CV fold: Fold1 Classification: using 'mean misclassification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean misclassification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean misclassification error' as optimization metric. CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Classification: using 'mean misclassification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean misclassification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean misclassification error' as optimization metric. CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Classification: using 'mean misclassification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean misclassification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean misclassification error' as optimization metric. CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Regression: using 'mean squared error' as optimization metric. elapsed = 0.08 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.45392 Regression: using 'mean squared error' as optimization metric. elapsed = 0.07 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.45392 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.45392 Regression: using 'mean squared error' as optimization metric. elapsed = 0.07 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.45392 Regression: using 'mean squared error' as optimization metric. elapsed = 0.08 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.45392 Regression: using 'mean squared error' as optimization metric. elapsed = 0.08 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.45392 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.45392 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.45392 Regression: using 'mean squared error' as optimization metric. elapsed = 0.07 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -27.95512 Regression: using 'mean squared error' as optimization metric. elapsed = 0.07 Round = 11 minsplit = 34.0000 cp = 0.0376811 maxdepth = 27.0000 Value = -23.45392 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 12 minsplit = 50.0000 cp = 0.01435421 maxdepth = 15.0000 Value = -23.45392 Best Parameters Found: Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392 Regression: using 'mean squared error' as optimization metric. Regression: using 'mean squared error' as optimization metric. Regression: using 'mean squared error' as optimization metric. CV fold: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Regression: using 'mean squared error' as optimization metric. elapsed = 0.08 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.46583 Regression: using 'mean squared error' as optimization metric. elapsed = 0.04 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.46583 Regression: using 'mean squared error' as optimization metric. elapsed = 0.07 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.46583 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.46583 Regression: using 'mean squared error' as optimization metric. elapsed = 0.07 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.46583 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.46583 Regression: using 'mean squared error' as optimization metric. elapsed = 0.05 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.46583 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.46583 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -32.38194 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -27.46583 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 12 minsplit = 74.0000 cp = 0.07391919 maxdepth = 30.0000 Value = -27.46583 Best Parameters Found: Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583 CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.77709 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.77709 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.77709 Regression: using 'mean squared error' as optimization metric. elapsed = 0.04 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.77709 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.77709 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.77709 Regression: using 'mean squared error' as optimization metric. elapsed = 0.07 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.77709 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.77709 Regression: using 'mean squared error' as optimization metric. elapsed = 0.05 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -28.80713 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -23.77709 Regression: using 'mean squared error' as optimization metric. elapsed = 0.04 Round = 12 minsplit = 19.0000 cp = 0.0304321 maxdepth = 30.0000 Value = -23.77709 Best Parameters Found: Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709 CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Regression: using 'mean squared error' as optimization metric. elapsed = 0.05 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913 Regression: using 'mean squared error' as optimization metric. elapsed = 0.05 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.74913 Regression: using 'mean squared error' as optimization metric. elapsed = 0.07 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.74913 Regression: using 'mean squared error' as optimization metric. elapsed = 0.04 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.74913 Regression: using 'mean squared error' as optimization metric. elapsed = 0.07 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.74913 Regression: using 'mean squared error' as optimization metric. elapsed = 0.05 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.74913 Regression: using 'mean squared error' as optimization metric. elapsed = 0.08 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.74913 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.74913 Regression: using 'mean squared error' as optimization metric. elapsed = 0.08 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.74913 Regression: using 'mean squared error' as optimization metric. elapsed = 0.05 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -35.47854 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 11 minsplit = 10.0000 cp = 0.06931338 maxdepth = 20.0000 Value = -27.74913 Regression: using 'mean squared error' as optimization metric. elapsed = 0.06 Round = 12 minsplit = 4.0000 cp = 0.07846899 maxdepth = 30.0000 Value = -27.74913 Best Parameters Found: Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913 CV fold: Fold1 Regression: using 'mean squared error' as optimization metric. Regression: using 'mean squared error' as optimization metric. Regression: using 'mean squared error' as optimization metric. CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Regression: using 'mean squared error' as optimization metric. Regression: using 'mean squared error' as optimization metric. Regression: using 'mean squared error' as optimization metric. CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Regression: using 'mean squared error' as optimization metric. Regression: using 'mean squared error' as optimization metric. Regression: using 'mean squared error' as optimization metric. [ FAIL 0 | WARN 0 | SKIP 1 | PASS 78 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • On CRAN (1): 'test-lints.R:10:5' [ FAIL 0 | WARN 0 | SKIP 1 | PASS 78 ] > > proc.time() user system elapsed 325.20 14.20 341.36