Nothing
Code
tune_bayes(wflow, resamples = folds, param_info = pset, initial = iter1, iter = iter2,
control = control_bayes(verbose = TRUE))
Message
> Generating a set of 2 initial parameter results
v Initialization complete
i Gaussian process model
! The Gaussian process model is being fit using 1 features but only has 2
data points to do so. This may cause errors or a poor model fit.
! Gaussian process model: X should be in range (0, 1)
v Gaussian process model
i Generating 3 candidates
i Predicted candidates
i Estimating performance
i Fold01: preprocessor 1/1
v Fold01: preprocessor 1/1
i Fold01: preprocessor 1/1, model 1/1
v Fold01: preprocessor 1/1, model 1/1
i Fold01: preprocessor 1/1, model 1/1 (extracts)
i Fold01: preprocessor 1/1, model 1/1 (predictions)
i Fold02: preprocessor 1/1
v Fold02: preprocessor 1/1
i Fold02: preprocessor 1/1, model 1/1
v Fold02: preprocessor 1/1, model 1/1
i Fold02: preprocessor 1/1, model 1/1 (extracts)
i Fold02: preprocessor 1/1, model 1/1 (predictions)
i Fold03: preprocessor 1/1
v Fold03: preprocessor 1/1
i Fold03: preprocessor 1/1, model 1/1
v Fold03: preprocessor 1/1, model 1/1
i Fold03: preprocessor 1/1, model 1/1 (extracts)
i Fold03: preprocessor 1/1, model 1/1 (predictions)
i Fold04: preprocessor 1/1
v Fold04: preprocessor 1/1
i Fold04: preprocessor 1/1, model 1/1
v Fold04: preprocessor 1/1, model 1/1
i Fold04: preprocessor 1/1, model 1/1 (extracts)
i Fold04: preprocessor 1/1, model 1/1 (predictions)
i Fold05: preprocessor 1/1
v Fold05: preprocessor 1/1
i Fold05: preprocessor 1/1, model 1/1
v Fold05: preprocessor 1/1, model 1/1
i Fold05: preprocessor 1/1, model 1/1 (extracts)
i Fold05: preprocessor 1/1, model 1/1 (predictions)
i Fold06: preprocessor 1/1
v Fold06: preprocessor 1/1
i Fold06: preprocessor 1/1, model 1/1
v Fold06: preprocessor 1/1, model 1/1
i Fold06: preprocessor 1/1, model 1/1 (extracts)
i Fold06: preprocessor 1/1, model 1/1 (predictions)
i Fold07: preprocessor 1/1
v Fold07: preprocessor 1/1
i Fold07: preprocessor 1/1, model 1/1
v Fold07: preprocessor 1/1, model 1/1
i Fold07: preprocessor 1/1, model 1/1 (extracts)
i Fold07: preprocessor 1/1, model 1/1 (predictions)
i Fold08: preprocessor 1/1
v Fold08: preprocessor 1/1
i Fold08: preprocessor 1/1, model 1/1
v Fold08: preprocessor 1/1, model 1/1
i Fold08: preprocessor 1/1, model 1/1 (extracts)
i Fold08: preprocessor 1/1, model 1/1 (predictions)
i Fold09: preprocessor 1/1
v Fold09: preprocessor 1/1
i Fold09: preprocessor 1/1, model 1/1
v Fold09: preprocessor 1/1, model 1/1
i Fold09: preprocessor 1/1, model 1/1 (extracts)
i Fold09: preprocessor 1/1, model 1/1 (predictions)
i Fold10: preprocessor 1/1
v Fold10: preprocessor 1/1
i Fold10: preprocessor 1/1, model 1/1
v Fold10: preprocessor 1/1, model 1/1
i Fold10: preprocessor 1/1, model 1/1 (extracts)
i Fold10: preprocessor 1/1, model 1/1 (predictions)
v Estimating performance
i Gaussian process model
! Gaussian process model: X should be in range (0, 1)
v Gaussian process model
i Generating 2 candidates
i Predicted candidates
i Estimating performance
i Fold01: preprocessor 1/1
v Fold01: preprocessor 1/1
i Fold01: preprocessor 1/1, model 1/1
v Fold01: preprocessor 1/1, model 1/1
i Fold01: preprocessor 1/1, model 1/1 (extracts)
i Fold01: preprocessor 1/1, model 1/1 (predictions)
i Fold02: preprocessor 1/1
v Fold02: preprocessor 1/1
i Fold02: preprocessor 1/1, model 1/1
v Fold02: preprocessor 1/1, model 1/1
i Fold02: preprocessor 1/1, model 1/1 (extracts)
i Fold02: preprocessor 1/1, model 1/1 (predictions)
i Fold03: preprocessor 1/1
v Fold03: preprocessor 1/1
i Fold03: preprocessor 1/1, model 1/1
v Fold03: preprocessor 1/1, model 1/1
i Fold03: preprocessor 1/1, model 1/1 (extracts)
i Fold03: preprocessor 1/1, model 1/1 (predictions)
i Fold04: preprocessor 1/1
v Fold04: preprocessor 1/1
i Fold04: preprocessor 1/1, model 1/1
v Fold04: preprocessor 1/1, model 1/1
i Fold04: preprocessor 1/1, model 1/1 (extracts)
i Fold04: preprocessor 1/1, model 1/1 (predictions)
i Fold05: preprocessor 1/1
v Fold05: preprocessor 1/1
i Fold05: preprocessor 1/1, model 1/1
v Fold05: preprocessor 1/1, model 1/1
i Fold05: preprocessor 1/1, model 1/1 (extracts)
i Fold05: preprocessor 1/1, model 1/1 (predictions)
i Fold06: preprocessor 1/1
v Fold06: preprocessor 1/1
i Fold06: preprocessor 1/1, model 1/1
v Fold06: preprocessor 1/1, model 1/1
i Fold06: preprocessor 1/1, model 1/1 (extracts)
i Fold06: preprocessor 1/1, model 1/1 (predictions)
i Fold07: preprocessor 1/1
v Fold07: preprocessor 1/1
i Fold07: preprocessor 1/1, model 1/1
v Fold07: preprocessor 1/1, model 1/1
i Fold07: preprocessor 1/1, model 1/1 (extracts)
i Fold07: preprocessor 1/1, model 1/1 (predictions)
i Fold08: preprocessor 1/1
v Fold08: preprocessor 1/1
i Fold08: preprocessor 1/1, model 1/1
v Fold08: preprocessor 1/1, model 1/1
i Fold08: preprocessor 1/1, model 1/1 (extracts)
i Fold08: preprocessor 1/1, model 1/1 (predictions)
i Fold09: preprocessor 1/1
v Fold09: preprocessor 1/1
i Fold09: preprocessor 1/1, model 1/1
v Fold09: preprocessor 1/1, model 1/1
i Fold09: preprocessor 1/1, model 1/1 (extracts)
i Fold09: preprocessor 1/1, model 1/1 (predictions)
i Fold10: preprocessor 1/1
v Fold10: preprocessor 1/1
i Fold10: preprocessor 1/1, model 1/1
v Fold10: preprocessor 1/1, model 1/1
i Fold10: preprocessor 1/1, model 1/1 (extracts)
i Fold10: preprocessor 1/1, model 1/1 (predictions)
v Estimating performance
Output
# Tuning results
# 10-fold cross-validation
# A tibble: 30 x 5
splits id .metrics .notes .iter
<list> <chr> <list> <list> <int>
1 <split [28/4]> Fold01 <tibble [4 x 5]> <tibble [0 x 3]> 0
2 <split [28/4]> Fold02 <tibble [4 x 5]> <tibble [0 x 3]> 0
3 <split [29/3]> Fold03 <tibble [4 x 5]> <tibble [0 x 3]> 0
4 <split [29/3]> Fold04 <tibble [4 x 5]> <tibble [0 x 3]> 0
5 <split [29/3]> Fold05 <tibble [4 x 5]> <tibble [0 x 3]> 0
6 <split [29/3]> Fold06 <tibble [4 x 5]> <tibble [0 x 3]> 0
7 <split [29/3]> Fold07 <tibble [4 x 5]> <tibble [0 x 3]> 0
8 <split [29/3]> Fold08 <tibble [4 x 5]> <tibble [0 x 3]> 0
9 <split [29/3]> Fold09 <tibble [4 x 5]> <tibble [0 x 3]> 0
10 <split [29/3]> Fold10 <tibble [4 x 5]> <tibble [0 x 3]> 0
# i 20 more rows
Code
tune_bayes(wflow, resamples = folds, param_info = pset, initial = iter1, iter = iter2,
control = control_bayes(verbose_iter = TRUE))
Message
Optimizing rmse using the expected improvement
-- Iteration 1 -----------------------------------------------------------------
i Current best: rmse=2.418 (@iter 0)
i Gaussian process model
! The Gaussian process model is being fit using 1 features but only has 2
data points to do so. This may cause errors or a poor model fit.
! Gaussian process model: X should be in range (0, 1)
v Gaussian process model
i Generating 3 candidates
i Predicted candidates
i num_comp=4
i Estimating performance
v Estimating performance
(x) Newest results: rmse=2.461 (+/-0.37)
-- Iteration 2 -----------------------------------------------------------------
i Current best: rmse=2.418 (@iter 0)
i Gaussian process model
! Gaussian process model: X should be in range (0, 1)
v Gaussian process model
i Generating 2 candidates
i Predicted candidates
i num_comp=5
i Estimating performance
v Estimating performance
(x) Newest results: rmse=2.453 (+/-0.381)
Output
# Tuning results
# 10-fold cross-validation
# A tibble: 30 x 5
splits id .metrics .notes .iter
<list> <chr> <list> <list> <int>
1 <split [28/4]> Fold01 <tibble [4 x 5]> <tibble [0 x 3]> 0
2 <split [28/4]> Fold02 <tibble [4 x 5]> <tibble [0 x 3]> 0
3 <split [29/3]> Fold03 <tibble [4 x 5]> <tibble [0 x 3]> 0
4 <split [29/3]> Fold04 <tibble [4 x 5]> <tibble [0 x 3]> 0
5 <split [29/3]> Fold05 <tibble [4 x 5]> <tibble [0 x 3]> 0
6 <split [29/3]> Fold06 <tibble [4 x 5]> <tibble [0 x 3]> 0
7 <split [29/3]> Fold07 <tibble [4 x 5]> <tibble [0 x 3]> 0
8 <split [29/3]> Fold08 <tibble [4 x 5]> <tibble [0 x 3]> 0
9 <split [29/3]> Fold09 <tibble [4 x 5]> <tibble [0 x 3]> 0
10 <split [29/3]> Fold10 <tibble [4 x 5]> <tibble [0 x 3]> 0
# i 20 more rows
Code
tune_bayes(wflow, resamples = folds, param_info = pset, initial = iter1, iter = iter2,
control = control_bayes(verbose_iter = TRUE, verbose = TRUE))
Message
> Generating a set of 2 initial parameter results
v Initialization complete
Optimizing rmse using the expected improvement
-- Iteration 1 -----------------------------------------------------------------
i Current best: rmse=2.418 (@iter 0)
i Gaussian process model
! The Gaussian process model is being fit using 1 features but only has 2
data points to do so. This may cause errors or a poor model fit.
! Gaussian process model: X should be in range (0, 1)
v Gaussian process model
i Generating 3 candidates
i Predicted candidates
i num_comp=2
i Estimating performance
i Fold01: preprocessor 1/1
v Fold01: preprocessor 1/1
i Fold01: preprocessor 1/1, model 1/1
v Fold01: preprocessor 1/1, model 1/1
i Fold01: preprocessor 1/1, model 1/1 (extracts)
i Fold01: preprocessor 1/1, model 1/1 (predictions)
i Fold02: preprocessor 1/1
v Fold02: preprocessor 1/1
i Fold02: preprocessor 1/1, model 1/1
v Fold02: preprocessor 1/1, model 1/1
i Fold02: preprocessor 1/1, model 1/1 (extracts)
i Fold02: preprocessor 1/1, model 1/1 (predictions)
i Fold03: preprocessor 1/1
v Fold03: preprocessor 1/1
i Fold03: preprocessor 1/1, model 1/1
v Fold03: preprocessor 1/1, model 1/1
i Fold03: preprocessor 1/1, model 1/1 (extracts)
i Fold03: preprocessor 1/1, model 1/1 (predictions)
i Fold04: preprocessor 1/1
v Fold04: preprocessor 1/1
i Fold04: preprocessor 1/1, model 1/1
v Fold04: preprocessor 1/1, model 1/1
i Fold04: preprocessor 1/1, model 1/1 (extracts)
i Fold04: preprocessor 1/1, model 1/1 (predictions)
i Fold05: preprocessor 1/1
v Fold05: preprocessor 1/1
i Fold05: preprocessor 1/1, model 1/1
v Fold05: preprocessor 1/1, model 1/1
i Fold05: preprocessor 1/1, model 1/1 (extracts)
i Fold05: preprocessor 1/1, model 1/1 (predictions)
i Fold06: preprocessor 1/1
v Fold06: preprocessor 1/1
i Fold06: preprocessor 1/1, model 1/1
v Fold06: preprocessor 1/1, model 1/1
i Fold06: preprocessor 1/1, model 1/1 (extracts)
i Fold06: preprocessor 1/1, model 1/1 (predictions)
i Fold07: preprocessor 1/1
v Fold07: preprocessor 1/1
i Fold07: preprocessor 1/1, model 1/1
v Fold07: preprocessor 1/1, model 1/1
i Fold07: preprocessor 1/1, model 1/1 (extracts)
i Fold07: preprocessor 1/1, model 1/1 (predictions)
i Fold08: preprocessor 1/1
v Fold08: preprocessor 1/1
i Fold08: preprocessor 1/1, model 1/1
v Fold08: preprocessor 1/1, model 1/1
i Fold08: preprocessor 1/1, model 1/1 (extracts)
i Fold08: preprocessor 1/1, model 1/1 (predictions)
i Fold09: preprocessor 1/1
v Fold09: preprocessor 1/1
i Fold09: preprocessor 1/1, model 1/1
v Fold09: preprocessor 1/1, model 1/1
i Fold09: preprocessor 1/1, model 1/1 (extracts)
i Fold09: preprocessor 1/1, model 1/1 (predictions)
i Fold10: preprocessor 1/1
v Fold10: preprocessor 1/1
i Fold10: preprocessor 1/1, model 1/1
v Fold10: preprocessor 1/1, model 1/1
i Fold10: preprocessor 1/1, model 1/1 (extracts)
i Fold10: preprocessor 1/1, model 1/1 (predictions)
v Estimating performance
(x) Newest results: rmse=2.666 (+/-0.281)
-- Iteration 2 -----------------------------------------------------------------
i Current best: rmse=2.418 (@iter 0)
i Gaussian process model
! Gaussian process model: X should be in range (0, 1)
v Gaussian process model
i Generating 2 candidates
i Predicted candidates
i num_comp=5
i Estimating performance
i Fold01: preprocessor 1/1
v Fold01: preprocessor 1/1
i Fold01: preprocessor 1/1, model 1/1
v Fold01: preprocessor 1/1, model 1/1
i Fold01: preprocessor 1/1, model 1/1 (extracts)
i Fold01: preprocessor 1/1, model 1/1 (predictions)
i Fold02: preprocessor 1/1
v Fold02: preprocessor 1/1
i Fold02: preprocessor 1/1, model 1/1
v Fold02: preprocessor 1/1, model 1/1
i Fold02: preprocessor 1/1, model 1/1 (extracts)
i Fold02: preprocessor 1/1, model 1/1 (predictions)
i Fold03: preprocessor 1/1
v Fold03: preprocessor 1/1
i Fold03: preprocessor 1/1, model 1/1
v Fold03: preprocessor 1/1, model 1/1
i Fold03: preprocessor 1/1, model 1/1 (extracts)
i Fold03: preprocessor 1/1, model 1/1 (predictions)
i Fold04: preprocessor 1/1
v Fold04: preprocessor 1/1
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v Fold04: preprocessor 1/1, model 1/1
i Fold04: preprocessor 1/1, model 1/1 (extracts)
i Fold04: preprocessor 1/1, model 1/1 (predictions)
i Fold05: preprocessor 1/1
v Fold05: preprocessor 1/1
i Fold05: preprocessor 1/1, model 1/1
v Fold05: preprocessor 1/1, model 1/1
i Fold05: preprocessor 1/1, model 1/1 (extracts)
i Fold05: preprocessor 1/1, model 1/1 (predictions)
i Fold06: preprocessor 1/1
v Fold06: preprocessor 1/1
i Fold06: preprocessor 1/1, model 1/1
v Fold06: preprocessor 1/1, model 1/1
i Fold06: preprocessor 1/1, model 1/1 (extracts)
i Fold06: preprocessor 1/1, model 1/1 (predictions)
i Fold07: preprocessor 1/1
v Fold07: preprocessor 1/1
i Fold07: preprocessor 1/1, model 1/1
v Fold07: preprocessor 1/1, model 1/1
i Fold07: preprocessor 1/1, model 1/1 (extracts)
i Fold07: preprocessor 1/1, model 1/1 (predictions)
i Fold08: preprocessor 1/1
v Fold08: preprocessor 1/1
i Fold08: preprocessor 1/1, model 1/1
v Fold08: preprocessor 1/1, model 1/1
i Fold08: preprocessor 1/1, model 1/1 (extracts)
i Fold08: preprocessor 1/1, model 1/1 (predictions)
i Fold09: preprocessor 1/1
v Fold09: preprocessor 1/1
i Fold09: preprocessor 1/1, model 1/1
v Fold09: preprocessor 1/1, model 1/1
i Fold09: preprocessor 1/1, model 1/1 (extracts)
i Fold09: preprocessor 1/1, model 1/1 (predictions)
i Fold10: preprocessor 1/1
v Fold10: preprocessor 1/1
i Fold10: preprocessor 1/1, model 1/1
v Fold10: preprocessor 1/1, model 1/1
i Fold10: preprocessor 1/1, model 1/1 (extracts)
i Fold10: preprocessor 1/1, model 1/1 (predictions)
v Estimating performance
(x) Newest results: rmse=2.453 (+/-0.381)
Output
# Tuning results
# 10-fold cross-validation
# A tibble: 30 x 5
splits id .metrics .notes .iter
<list> <chr> <list> <list> <int>
1 <split [28/4]> Fold01 <tibble [4 x 5]> <tibble [0 x 3]> 0
2 <split [28/4]> Fold02 <tibble [4 x 5]> <tibble [0 x 3]> 0
3 <split [29/3]> Fold03 <tibble [4 x 5]> <tibble [0 x 3]> 0
4 <split [29/3]> Fold04 <tibble [4 x 5]> <tibble [0 x 3]> 0
5 <split [29/3]> Fold05 <tibble [4 x 5]> <tibble [0 x 3]> 0
6 <split [29/3]> Fold06 <tibble [4 x 5]> <tibble [0 x 3]> 0
7 <split [29/3]> Fold07 <tibble [4 x 5]> <tibble [0 x 3]> 0
8 <split [29/3]> Fold08 <tibble [4 x 5]> <tibble [0 x 3]> 0
9 <split [29/3]> Fold09 <tibble [4 x 5]> <tibble [0 x 3]> 0
10 <split [29/3]> Fold10 <tibble [4 x 5]> <tibble [0 x 3]> 0
# i 20 more rows
Code
cars_res <- tune_bayes(svm_mod, preprocessor = rec, resamples = data_folds)
Message
x Fold1: preprocessor 1/1:
Error in `step_bs()`:
Caused by error in `if (...) NULL`:
! missing value where TRUE/FALSE needed
x Fold2: preprocessor 1/1:
Error in `step_bs()`:
Caused by error in `if (...) NULL`:
! missing value where TRUE/FALSE needed
Condition
Warning:
All models failed. Run `show_notes(.Last.tune.result)` for more information.
Message
x Optimization stopped prematurely; returning current results.
Code
cars_res <- tune_bayes(wflow, resamples = data_folds, control = control_bayes(
extract = function(x) {
1
}, save_pred = TRUE))
Message
x Fold1: preprocessor 1/1:
Error in `get_all_predictors()`:
! The following predictors were not found in `data`: 'z'.
x Fold2: preprocessor 1/1:
Error in `get_all_predictors()`:
! The following predictors were not found in `data`: 'z'.
Condition
Warning:
All models failed. Run `show_notes(.Last.tune.result)` for more information.
Message
x Optimization stopped prematurely; returning current results.
Code
tune_bayes(rec_tune_1, model = lm_mod, resamples = rsample::vfold_cv(mtcars, v = 2),
param_info = extract_parameter_set_dials(rec_tune_1), iter = iter1, initial = iter2)
Condition
Error in `tune_bayes()`:
! The first argument to [tune_bayes()] should be either a model or workflow.
Code
tune_bayes(mpg ~ ., svm_mod, resamples = rsample::vfold_cv(mtcars, v = 2),
param_info = extract_parameter_set_dials(svm_mod), initial = iter1, iter = iter2)
Condition
Error in `tune_bayes()`:
! The first argument to [tune_bayes()] should be either a model or workflow.
Code
res2 <- tune_bayes(wflow, resamples = folds, param_info = pset, initial = iter1,
iter = iter2, control = control_bayes(save_workflow = TRUE))
Message
! The Gaussian process model is being fit using 1 features but only has 2
data points to do so. This may cause errors or a poor model fit.
! Gaussian process model: X should be in range (0, 1)
Code
tune:::check_bayes_initial_size(5, 3, FALSE)
Message
! There are 5 tuning parameters and 3 grid points were requested.
* There are more tuning parameters than there are initial points. This is
likely to cause numerical issues in the first few search iterations.
Code
tune:::check_bayes_initial_size(5, 3, TRUE)
Message
! There are 5 tuning parameters and 3 grid points were requested.
* There are more tuning parameters than there are initial points. This is
likely to cause numerical issues in the first few search iterations.
* With racing, only completely resampled parameters are used.
Code
tune:::check_bayes_initial_size(2, 2, FALSE)
Message
! There are 2 tuning parameters and 2 grid points were requested.
* There are as many tuning parameters as there are initial points. This is
likely to cause numerical issues in the first few search iterations.
Code
tune:::check_bayes_initial_size(5, 1, FALSE)
Condition
Error:
! There are 5 tuning parameters and 1 grid point was requested.
* The GP model requires 2+ initial points. For best performance, supply more initial points than there are tuning parameters.
Code
tune:::check_bayes_initial_size(5, 1, TRUE)
Condition
Error:
! There are 5 tuning parameters and 1 grid point was requested.
* The GP model requires 2+ initial points. For best performance, supply more initial points than there are tuning parameters.
* With racing, only completely resampled parameters are used.
Code
tune:::check_bayes_initial_size(1, 1, FALSE)
Condition
Error:
! There is 1 tuning parameter and 1 grid point was requested.
* The GP model requires 2+ initial points. For best performance, supply more initial points than there are tuning parameters.
Code
set.seed(1)
res <- mod %>% tune_bayes(Sale_Price ~ Neighborhood + Gr_Liv_Area + Year_Built +
Bldg_Type + Latitude + Longitude, resamples = folds, initial = 3, metrics = yardstick::metric_set(
rsq), param_info = parameters(dials::cost_complexity(c(-2, 0))))
Message
! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
! For the rsq estimates, 1 missing value was found and removed before fitting
the Gaussian process model.
! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
! For the rsq estimates, 2 missing values were found and removed before
fitting the Gaussian process model.
! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
! For the rsq estimates, 3 missing values were found and removed before
fitting the Gaussian process model.
! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
! For the rsq estimates, 4 missing values were found and removed before
fitting the Gaussian process model.
! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
! For the rsq estimates, 5 missing values were found and removed before
fitting the Gaussian process model.
! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
! For the rsq estimates, 6 missing values were found and removed before
fitting the Gaussian process model.
! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
! For the rsq estimates, 7 missing values were found and removed before
fitting the Gaussian process model.
! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
Code
set.seed(2)
res_fail <- mod %>% tune_bayes(Sale_Price ~ Neighborhood + Gr_Liv_Area +
Year_Built + Bldg_Type + Latitude + Longitude, resamples = folds, initial = 5,
metrics = yardstick::metric_set(rsq), param_info = parameters(dials::cost_complexity(
c(0.5, 0))))
Message
! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
! All of the rsq estimates were missing. The Gaussian process model cannot be
fit to the data.
! Gaussian process model: no non-missing arguments to min; returning Inf, no non-missing arguments...
x Gaussian process model: Error in seq_len(n - 1L): argument must be coercible to non-negative int...
Condition
Error in `check_gp_failure()`:
! Gaussian process model was not fit.
Message
x Optimization stopped prematurely; returning current results.
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