**Overview**

I have produced four models using the tidymodels package with the data frame FID (see below):

- General Linear Model
- Bagged Tree
- Random Forest
- Boosted Trees

The data frame FID contains three predictors:

- Year (numeric)
- Month (Factor)
- Days (numeric)

**The dependent variable is Frequency (numeric)**

I am attempting to tune my a bagged tree model produced by using the function bag_tree() in the baguette package, and I am experiencing issues using the tune_grid() function.

If anyone can help, I would be deeply appreciative.

Many thanks.

**Error Message**

```
#Error messages
! Fold02: internal: A correlation computation is required, but `estimate` is constant and has 0 standard deviation, resulting in a divide b...
! Fold07: internal: A correlation computation is required, but `estimate` is constant and has 0 standard deviation, resulting in a divide b...
! Fold08: internal: A correlation computation is required, but `estimate` is constant and has 0 standard deviation, resulting in a divide b...
! Fold10: internal: A correlation computation is required, but `estimate` is constant and has 0 sta...
Warning message:
This tuning result has notes. Example notes on model fitting include:
internal: A correlation computation is required, but `estimate` is constant and has 0 standard deviation, resulting in a divide by 0 error. `NA` will be returned.
internal: A correlation computation is required, but `estimate` is constant and has 0 standard deviation, resulting in a divide by 0 error. `NA` will be returned.
internal: A correlation computation is required, but `estimate` is constant and has 0 standard deviation, resulting in a divide by 0 error. `NA` will be returned.
```

**R-Code**

```
library(baguette)
library(tidymodels)
seed(45L)
#split this single dataset into two: a training set and a testing set
data_split <- initial_split(FID)
##Create data frames for the two sets:
train_data <- training(data_split)
test_data <- testing(data_split)
##resample the data with 10-fold cross-validation (10-fold by default)
cv <- vfold_cv(train_data, v=10)
###########################################################
##Produce the recipe
rec <- recipe(Frequency ~ ., data = FID) %>%
step_nzv(all_predictors(), freq_cut = 0, unique_cut = 0) %>% # remove variables with zero variances
step_novel(all_nominal()) %>% # prepares test data to handle previously unseen factor levels
step_medianimpute(all_numeric(), -all_outcomes(), -has_role("id vars")) %>% # replaces missing numeric observations with the median
step_dummy(all_nominal(), -has_role("id vars")) # dummy codes categorical variables
#########################################################
#########################################################
##Bagged Tree Model
#########################################################
#########################################################
##Produice the model
mod_bag <- bag_tree() %>%
set_mode("regression") %>%
set_engine("rpart", times = 10) #10 bootstrap resamples
##Create workflow
wflow_bag <- workflow() %>%
add_recipe(rec) %>%
add_model(mod_bag)
##Fit the model
plan(multisession)
fit_bag <- fit_resamples(
wflow_bag,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(save_pred = TRUE,
extract = function(x) extract_model(x)))
##Collect the metrics for the bagged trees
fit_bag %>% collect_metrics()
##Collect model predictions for each fold for the number of blue whale sightings
bag_predictions<-fit_bag %>% collect_predictions()
#######Tuning hyperparameters
##Estimating the best value model by estimating the best value by
##training many models on resamples data sets
##and exploring how well these models perform
tune_spec_bag <-
bag_tree(tree_depth = tune()) %>%
set_mode("regression") %>%
set_engine("rpart", times = 10)
#Create a regular grid of values to try using a convenience function
bag_grid <- grid_regular(
tree_depth(),
levels = 10
)
#Create the workflow for the tuned bagged model
bag_wf <- workflow() %>%
add_formula(Frequency ~ .) %>%
add_model(tune_spec_bag)
#Tune the bagged tree model
bag_res <- tune_grid(
wflow_bag %>% update_model(tune_spec_bag),
cv,
grid = bag_grid,
metrics=metric_set(rmse, rsq)
control = control_resamples(save_pred = TRUE)
)
#Error messages
! Fold02: internal: A correlation computation is required, but `estimate` is constant and has 0 standard deviation, resulting in a divide b...
! Fold07: internal: A correlation computation is required, but `estimate` is constant and has 0 standard deviation, resulting in a divide b...
! Fold08: internal: A correlation computation is required, but `estimate` is constant and has 0 standard deviation, resulting in a divide b...
! Fold10: internal: A correlation computation is required, but `estimate` is constant and has 0 sta...
Warning message:
This tuning result has notes. Example notes on model fitting include:
internal: A correlation computation is required, but `estimate` is constant and has 0 standard deviation, resulting in a divide by 0 error. `NA` will be returned.
internal: A correlation computation is required, but `estimate` is constant and has 0 standard deviation, resulting in a divide by 0 error. `NA` will be returned.
internal: A correlation computation is required, but `estimate` is constant and has 0 standard deviation, resulting in a divide by 0 error. `NA` will be returned.
```

**Data Frame - FID**

```
structure(list(Year = c(2015, 2015, 2015, 2015, 2015, 2015, 2015,
2015, 2015, 2015, 2015, 2015, 2016, 2016, 2016, 2016, 2016, 2016,
2016, 2016, 2016, 2016, 2016, 2016, 2017, 2017, 2017, 2017, 2017,
2017, 2017, 2017, 2017, 2017, 2017, 2017), Month = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L), .Label = c("January", "February", "March",
"April", "May", "June", "July", "August", "September", "October",
"November", "December"), class = "factor"), Frequency = c(36,
28, 39, 46, 5, 0, 0, 22, 10, 15, 8, 33, 33, 29, 31, 23, 8, 9,
7, 40, 41, 41, 30, 30, 44, 37, 41, 42, 20, 0, 7, 27, 35, 27,
43, 38), Days = c(31, 28, 31, 30, 6, 0, 0, 29, 15,
29, 29, 31, 31, 29, 30, 30, 7, 0, 7, 30, 30, 31, 30, 27, 31,
28, 30, 30, 21, 0, 7, 26, 29, 27, 29, 29)), row.names = c(NA,
-36L), class = "data.frame")
```