Running 'testthat.R' [490s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > # 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")
  Saving _problems/test-fold_equality-5.R
  Saving _problems/test-glm-5.R
  Saving _problems/test-glm_predictions-5.R
  
  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 = 5.67	Round = 1	k = 16.0000	Value = -0.1487759 
  elapsed = 5.39	Round = 2	k = 64.0000	Value = -0.123666 
  elapsed = 5.42	Round = 3	k = 10.0000	Value = -0.1638418 
  elapsed = 5.42	Round = 4	k = 34.0000	Value = -0.1321406 
  elapsed = 5.78	Round = 5	k = 80.0000	Value = -0.1217828 
  elapsed = 5.20	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 = 5.30	Round = 1	k = 16.0000	Value = -0.1487759 
  elapsed = 5.36	Round = 2	k = 64.0000	Value = -0.123666 
  elapsed = 5.08	Round = 3	k = 10.0000	Value = -0.1638418 
  elapsed = 5.95	Round = 4	k = 34.0000	Value = -0.1321406 
  elapsed = 5.18	Round = 5	k = 80.0000	Value = -0.1217828 
  elapsed = 5.96	Round = 6	k = 50.0000	Value = -0.1246077 
  
   Best Parameters Found: 
  Round = 5	k = 80.0000	Value = -0.1217828 
  elapsed = 5.01	Round = 1	k = 24.0000	Value = -0.1440678 
  elapsed = 5.47	Round = 2	k = 63.0000	Value = -0.1233522 
  elapsed = 5.44	Round = 3	k = 34.0000	Value = -0.1321406 
  elapsed = 5.18	Round = 4	k = 71.0000	Value = -0.1220967 
  elapsed = 5.53	Round = 5	k = 2.0000	Value = -0.2743252 
  elapsed = 5.64	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 = 1.34	Round = 1	k = 16.0000	Value = -0.1520798 
  elapsed = 1.46	Round = 2	k = 64.0000	Value = -0.1313681 
  elapsed = 1.61	Round = 3	k = 10.0000	Value = -0.1859821 
  elapsed = 1.97	Round = 4	k = 34.0000	Value = -0.1398453 
  elapsed = 2.00	Round = 5	k = 65.0000	Value = -0.132307 
  elapsed = 1.78	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 = 1.38	Round = 1	k = 16.0000	Value = -0.1577268 
  elapsed = 1.65	Round = 2	k = 64.0000	Value = -0.1153539 
  elapsed = 1.27	Round = 3	k = 10.0000	Value = -0.1732636 
  elapsed = 1.90	Round = 4	k = 34.0000	Value = -0.1360669 
  elapsed = 1.59	Round = 5	k = 80.0000	Value = -0.1082897 
  elapsed = 1.42	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 = 1.68	Round = 1	k = 16.0000	Value = -0.1577195 
  elapsed = 1.47	Round = 2	k = 64.0000	Value = -0.1299503 
  elapsed = 1.58	Round = 3	k = 10.0000	Value = -0.1798522 
  elapsed = 1.53	Round = 4	k = 34.0000	Value = -0.1384196 
  elapsed = 1.39	Round = 5	k = 77.0000	Value = -0.1304132 
  elapsed = 1.64	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 = 3.83	Round = 1	minsplit = 2.0000	cp = 0.0700	maxdepth = 22.0000	Value = -0.09196485 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 4.27	Round = 2	minsplit = 32.0000	cp = 0.0200	maxdepth = 27.0000	Value = -0.09196485 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 4.07	Round = 3	minsplit = 72.0000	cp = 0.1000	maxdepth = 7.0000	Value = -0.09196485 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 4.08	Round = 4	minsplit = 32.0000	cp = 0.0900	maxdepth = 27.0000	Value = -0.09196485 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 4.20	Round = 5	minsplit = 52.0000	cp = 0.0200	maxdepth = 12.0000	Value = -0.09196485 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 4.37	Round = 6	minsplit = 2.0000	cp = 0.0400	maxdepth = 7.0000	Value = -0.09196485 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 4.67	Round = 7	minsplit = 12.0000	cp = 0.0400	maxdepth = 17.0000	Value = -0.09196485 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 4.46	Round = 8	minsplit = 32.0000	cp = 0.0600	maxdepth = 12.0000	Value = -0.09196485 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 3.69	Round = 9	minsplit = 2.0000	cp = 0.0800	maxdepth = 12.0000	Value = -0.09196485 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 3.38	Round = 10	minsplit = 42.0000	cp = 0.0200	maxdepth = 2.0000	Value = -0.2715003 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 3.55	Round = 11	minsplit = 100.0000	cp = 0.03567893	maxdepth = 4.0000	Value = -0.106403 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 3.55	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 = 1.63	Round = 1	minsplit = 2.0000	cp = 0.0700	maxdepth = 22.0000	Value = -0.09558117 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.62	Round = 2	minsplit = 32.0000	cp = 0.0200	maxdepth = 27.0000	Value = -0.09558117 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.48	Round = 3	minsplit = 72.0000	cp = 0.1000	maxdepth = 7.0000	Value = -0.09558117 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.54	Round = 4	minsplit = 32.0000	cp = 0.0900	maxdepth = 27.0000	Value = -0.09558117 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.77	Round = 5	minsplit = 52.0000	cp = 0.0200	maxdepth = 12.0000	Value = -0.09558117 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.59	Round = 6	minsplit = 2.0000	cp = 0.0400	maxdepth = 7.0000	Value = -0.09558117 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.58	Round = 7	minsplit = 12.0000	cp = 0.0400	maxdepth = 17.0000	Value = -0.09558117 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.43	Round = 8	minsplit = 32.0000	cp = 0.0600	maxdepth = 12.0000	Value = -0.09558117 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.66	Round = 9	minsplit = 2.0000	cp = 0.0800	maxdepth = 12.0000	Value = -0.09558117 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 0.93	Round = 10	minsplit = 42.0000	cp = 0.0200	maxdepth = 2.0000	Value = -0.2853118 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.58	Round = 11	minsplit = 83.0000	cp = 0.03306856	maxdepth = 6.0000	Value = -0.09746574 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.45	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 = 1.55	Round = 1	minsplit = 2.0000	cp = 0.0700	maxdepth = 22.0000	Value = -0.08333478 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.57	Round = 2	minsplit = 32.0000	cp = 0.0200	maxdepth = 27.0000	Value = -0.08333478 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.52	Round = 3	minsplit = 72.0000	cp = 0.1000	maxdepth = 7.0000	Value = -0.08333478 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.54	Round = 4	minsplit = 32.0000	cp = 0.0900	maxdepth = 27.0000	Value = -0.08333478 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.53	Round = 5	minsplit = 52.0000	cp = 0.0200	maxdepth = 12.0000	Value = -0.08333478 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.59	Round = 6	minsplit = 2.0000	cp = 0.0400	maxdepth = 7.0000	Value = -0.08333478 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.50	Round = 7	minsplit = 12.0000	cp = 0.0400	maxdepth = 17.0000	Value = -0.08333478 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.44	Round = 8	minsplit = 32.0000	cp = 0.0600	maxdepth = 12.0000	Value = -0.08333478 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.43	Round = 9	minsplit = 2.0000	cp = 0.0800	maxdepth = 12.0000	Value = -0.08333478 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 0.96	Round = 10	minsplit = 42.0000	cp = 0.0200	maxdepth = 2.0000	Value = -0.2806083 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.52	Round = 11	minsplit = 83.0000	cp = 0.03306856	maxdepth = 6.0000	Value = -0.08333478 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.52	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 = 1.65	Round = 1	minsplit = 2.0000	cp = 0.0700	maxdepth = 22.0000	Value = -0.1130091 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.58	Round = 2	minsplit = 32.0000	cp = 0.0200	maxdepth = 27.0000	Value = -0.1130091 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.48	Round = 3	minsplit = 72.0000	cp = 0.1000	maxdepth = 7.0000	Value = -0.1148897 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.50	Round = 4	minsplit = 32.0000	cp = 0.0900	maxdepth = 27.0000	Value = -0.1130091 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.59	Round = 5	minsplit = 52.0000	cp = 0.0200	maxdepth = 12.0000	Value = -0.1130091 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.44	Round = 6	minsplit = 2.0000	cp = 0.0400	maxdepth = 7.0000	Value = -0.1148897 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.43	Round = 7	minsplit = 12.0000	cp = 0.0400	maxdepth = 17.0000	Value = -0.1130091 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.58	Round = 8	minsplit = 32.0000	cp = 0.0600	maxdepth = 12.0000	Value = -0.1130091 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.56	Round = 9	minsplit = 2.0000	cp = 0.0800	maxdepth = 12.0000	Value = -0.1130091 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 0.95	Round = 10	minsplit = 42.0000	cp = 0.0200	maxdepth = 2.0000	Value = -0.2796667 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.80	Round = 11	minsplit = 28.0000	cp = 0.04674927	maxdepth = 14.0000	Value = -0.1130091 
  
   Classification: using 'mean misclassification error' as optimization metric.
  elapsed = 1.70	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 = 0.10	Round = 1	minsplit = 2.0000	cp = 0.0700	maxdepth = 22.0000	Value = -23.45392 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.11	Round = 2	minsplit = 32.0000	cp = 0.0200	maxdepth = 27.0000	Value = -23.45392 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.08	Round = 3	minsplit = 72.0000	cp = 0.1000	maxdepth = 7.0000	Value = -23.45392 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.11	Round = 4	minsplit = 32.0000	cp = 0.0900	maxdepth = 27.0000	Value = -23.45392 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.09	Round = 5	minsplit = 52.0000	cp = 0.0200	maxdepth = 12.0000	Value = -23.45392 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.09	Round = 6	minsplit = 2.0000	cp = 0.0400	maxdepth = 7.0000	Value = -23.45392 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.09	Round = 7	minsplit = 12.0000	cp = 0.0400	maxdepth = 17.0000	Value = -23.45392 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.08	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 = 0.08	Round = 10	minsplit = 42.0000	cp = 0.0200	maxdepth = 2.0000	Value = -27.95512 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.06	Round = 11	minsplit = 34.0000	cp = 0.0376811	maxdepth = 27.0000	Value = -23.45392 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.08	Round = 12	minsplit = 25.0000	cp = 0.05205146	maxdepth = 7.0000	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 = 0.09	Round = 2	minsplit = 32.0000	cp = 0.0200	maxdepth = 27.0000	Value = -27.46583 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.07	Round = 3	minsplit = 72.0000	cp = 0.1000	maxdepth = 7.0000	Value = -27.46583 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.04	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 = 0.09	Round = 6	minsplit = 2.0000	cp = 0.0400	maxdepth = 7.0000	Value = -27.46583 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.09	Round = 7	minsplit = 12.0000	cp = 0.0400	maxdepth = 17.0000	Value = -27.46583 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.06	Round = 8	minsplit = 32.0000	cp = 0.0600	maxdepth = 12.0000	Value = -27.46583 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.08	Round = 9	minsplit = 2.0000	cp = 0.0800	maxdepth = 12.0000	Value = -27.46583 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.08	Round = 10	minsplit = 42.0000	cp = 0.0200	maxdepth = 2.0000	Value = -32.38194 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.10	Round = 11	minsplit = 69.0000	cp = 0.05877216	maxdepth = 20.0000	Value = -27.46583 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.08	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 = 0.11	Round = 1	minsplit = 2.0000	cp = 0.0700	maxdepth = 22.0000	Value = -23.77709 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.11	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 = 0.07	Round = 4	minsplit = 32.0000	cp = 0.0900	maxdepth = 27.0000	Value = -23.77709 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.06	Round = 5	minsplit = 52.0000	cp = 0.0200	maxdepth = 12.0000	Value = -23.77709 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.11	Round = 6	minsplit = 2.0000	cp = 0.0400	maxdepth = 7.0000	Value = -23.77709 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.10	Round = 7	minsplit = 12.0000	cp = 0.0400	maxdepth = 17.0000	Value = -23.77709 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.09	Round = 8	minsplit = 32.0000	cp = 0.0600	maxdepth = 12.0000	Value = -23.77709 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.11	Round = 9	minsplit = 2.0000	cp = 0.0800	maxdepth = 12.0000	Value = -23.77709 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.09	Round = 10	minsplit = 42.0000	cp = 0.0200	maxdepth = 2.0000	Value = -28.80713 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.10	Round = 11	minsplit = 69.0000	cp = 0.05877216	maxdepth = 20.0000	Value = -23.77709 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.10	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 = 0.10	Round = 1	minsplit = 2.0000	cp = 0.0700	maxdepth = 22.0000	Value = -27.74913 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.09	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 = 0.09	Round = 4	minsplit = 32.0000	cp = 0.0900	maxdepth = 27.0000	Value = -27.74913 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.10	Round = 5	minsplit = 52.0000	cp = 0.0200	maxdepth = 12.0000	Value = -27.74913 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.11	Round = 6	minsplit = 2.0000	cp = 0.0400	maxdepth = 7.0000	Value = -27.74913 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.07	Round = 7	minsplit = 12.0000	cp = 0.0400	maxdepth = 17.0000	Value = -27.74913 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.09	Round = 8	minsplit = 32.0000	cp = 0.0600	maxdepth = 12.0000	Value = -27.74913 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.11	Round = 9	minsplit = 2.0000	cp = 0.0800	maxdepth = 12.0000	Value = -27.74913 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.09	Round = 10	minsplit = 42.0000	cp = 0.0200	maxdepth = 2.0000	Value = -35.47854 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.08	Round = 11	minsplit = 10.0000	cp = 0.06931338	maxdepth = 20.0000	Value = -27.74913 
  
   Regression: using 'mean squared error' as optimization metric.
  elapsed = 0.08	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.
  
  Parameter settings [=============================================] 3/3 (100%)
                                                                                
   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 3 | WARN 3 | SKIP 1 | PASS 58 ]
  
  ══ Skipped tests (1) ═══════════════════════════════════════════════════════════
  • On CRAN (1): 'test-lints.R:10:5'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-fold_equality.R:3:1'): (code run outside of `test_that()`) ─────
  Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
  Backtrace:
      ▆
   1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-fold_equality.R:3:1
   2. └─data.table::as.data.table(PimaIndiansDiabetes2)
  ── Error ('test-glm.R:3:1'): (code run outside of `test_that()`) ───────────────
  Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
  Backtrace:
      ▆
   1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-glm.R:3:1
   2. └─data.table::as.data.table(PimaIndiansDiabetes2)
  ── Error ('test-glm_predictions.R:3:1'): (code run outside of `test_that()`) ───
  Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
  Backtrace:
      ▆
   1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-glm_predictions.R:3:1
   2. └─data.table::as.data.table(PimaIndiansDiabetes2)
  
  [ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
  Error:
  ! Test failures.
  Execution halted