Machine Learning

LOADING REQUIRED PACKAGES

library(here)

here() starts at /Users/shiwanisapkota/Desktop/MADA Course/shiwanisapkota-MADA-portfolio

library(tidyverse)

── Attaching packages
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✔ tibble  3.1.8     ✔ dplyr   1.1.0
✔ tidyr   1.2.0     ✔ stringr 1.4.1
✔ readr   2.1.2     ✔ forcats 0.5.2
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library(tidymodels)

── Attaching packages ────────────────────────────────────── tidymodels 1.0.0 ──
✔ broom        1.0.1     ✔ rsample      1.1.1
✔ dials        1.1.0     ✔ tune         1.0.1
✔ infer        1.0.4     ✔ workflows    1.1.3
✔ modeldata    1.1.0     ✔ workflowsets 1.0.0
✔ parsnip      1.0.4     ✔ yardstick    1.1.0
✔ recipes      1.0.5     
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✖ yardstick::spec() masks readr::spec()
✖ recipes::step()   masks stats::step()
• Learn how to get started at https://www.tidymodels.org/start/

library(ggplot2)
library(ranger)
library(rpart)

Attaching package: 'rpart'

The following object is masked from 'package:dials':

    prune

library(glmnet)

Loading required package: Matrix

Attaching package: 'Matrix'

The following objects are masked from 'package:tidyr':

    expand, pack, unpack

Loaded glmnet 4.1-7

library(vip)

Attaching package: 'vip'

The following object is masked from 'package:utils':

    vi

library(rpart.plot)
library(performance)

Attaching package: 'performance'

The following objects are masked from 'package:yardstick':

    mae, rmse

library(yardstick)
library(recipes)

LOADING DATA

fludata_clean11 <- readRDS(here("fluanalysis", "data", "fludata_clean_module11.rds"))
glimpse(fludata_clean11)

Rows: 730
Columns: 26
$ SwollenLymphNodes <fct> Yes, Yes, Yes, Yes, Yes, No, No, No, Yes, No, Yes, Y…
$ ChestCongestion   <fct> No, Yes, Yes, Yes, No, No, No, Yes, Yes, Yes, Yes, Y…
$ ChillsSweats      <fct> No, No, Yes, Yes, Yes, Yes, Yes, Yes, Yes, No, Yes, …
$ NasalCongestion   <fct> No, Yes, Yes, Yes, No, No, No, Yes, Yes, Yes, Yes, Y…
$ Sneeze            <fct> No, No, Yes, Yes, No, Yes, No, Yes, No, No, No, No, …
$ Fatigue           <fct> Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Ye…
$ SubjectiveFever   <fct> Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, No, Yes…
$ Headache          <fct> Yes, Yes, Yes, Yes, Yes, Yes, No, Yes, Yes, Yes, Yes…
$ Weakness          <fct> Mild, Severe, Severe, Severe, Moderate, Moderate, Mi…
$ CoughIntensity    <fct> Severe, Severe, Mild, Moderate, None, Moderate, Seve…
$ Myalgia           <fct> Mild, Severe, Severe, Severe, Mild, Moderate, Mild, …
$ RunnyNose         <fct> No, No, Yes, Yes, No, No, Yes, Yes, Yes, Yes, No, No…
$ AbPain            <fct> No, No, Yes, No, No, No, No, No, No, No, Yes, Yes, N…
$ ChestPain         <fct> No, No, Yes, No, No, Yes, Yes, No, No, No, No, Yes, …
$ Diarrhea          <fct> No, No, No, No, No, Yes, No, No, No, No, No, No, No,…
$ EyePn             <fct> No, No, No, No, Yes, No, No, No, No, No, Yes, No, Ye…
$ Insomnia          <fct> No, No, Yes, Yes, Yes, No, No, Yes, Yes, Yes, Yes, Y…
$ ItchyEye          <fct> No, No, No, No, No, No, No, No, No, No, No, No, Yes,…
$ Nausea            <fct> No, No, Yes, Yes, Yes, Yes, No, No, Yes, Yes, Yes, Y…
$ EarPn             <fct> No, Yes, No, Yes, No, No, No, No, No, No, No, Yes, Y…
$ Pharyngitis       <fct> Yes, Yes, Yes, Yes, Yes, Yes, Yes, No, No, No, Yes, …
$ Breathless        <fct> No, No, Yes, No, No, Yes, No, No, No, Yes, No, Yes, …
$ ToothPn           <fct> No, No, Yes, No, No, No, No, No, Yes, No, No, Yes, N…
$ Vomit             <fct> No, No, No, No, No, No, Yes, No, No, No, Yes, Yes, N…
$ Wheeze            <fct> No, No, No, Yes, No, Yes, No, No, No, No, No, Yes, N…
$ BodyTemp          <dbl> 98.3, 100.4, 100.8, 98.8, 100.5, 98.4, 102.5, 98.4, …

DATA SETUP

# Setting the random seed to 123
set.seed(123)

# Splitting dataset into 70% training and 30% testing and using BodyTemp outcome as stratification
data_split <- initial_split(fludata_clean11, prop = 7/10, strata = BodyTemp)
  
# Creating data frames for the two sets:
train_data <- training(data_split)
test_data  <- testing(data_split)

# Creating 5-fold cross-validation, 5 times repeated
CV_fold_data <- vfold_cv(train_data, v = 5, repeats = 5, strata = BodyTemp)

# Creating a recipe
recipe_data <- recipe(BodyTemp ~ ., data = train_data) %>%
  step_dummy(all_nominal(), -all_outcomes())

NULL MODEL PERFORMANCE

# Using train data
# Creating a recipe for null model
recipe_null_train <- recipe(BodyTemp ~ 1, data = train_data) %>%
  step_dummy(all_nominal(), -all_outcomes())

# Creating a linear model recipe
recipe_null_logistic <- linear_reg() %>% 
  set_engine("lm") %>% 
  set_mode("regression")

# Creating workflow pairing model and recipe 
workflow_null_train <- workflow() %>% 
  add_model(recipe_null_logistic) %>% 
  add_recipe(recipe_null_train)

# Fitting null model with the folds created from train data 
train_null <- fit_resamples(workflow_null_train, resamples = CV_fold_data)

! Fold1, Repeat1: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold2, Repeat1: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold3, Repeat1: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold4, Repeat1: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold5, Repeat1: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold1, Repeat2: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold2, Repeat2: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold3, Repeat2: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold4, Repeat2: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold5, Repeat2: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold1, Repeat3: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold2, Repeat3: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold3, Repeat3: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold4, Repeat3: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold5, Repeat3: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold1, Repeat4: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold2, Repeat4: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold3, Repeat4: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold4, Repeat4: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold5, Repeat4: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold1, Repeat5: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold2, Repeat5: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold3, Repeat5: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold4, Repeat5: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold5, Repeat5: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

# Computing RMSE for train data
metrics_null_train <- collect_metrics(train_null)
metrics_null_train

# A tibble: 2 × 6
  .metric .estimator   mean     n std_err .config             
  <chr>   <chr>       <dbl> <int>   <dbl> <chr>               
1 rmse    standard     1.21    25  0.0177 Preprocessor1_Model1
2 rsq     standard   NaN        0 NA      Preprocessor1_Model1

From the null model, we got RMSE as 1.21.

MODEL TUNING AND FITTING: TREE MODEL

# Tuning hyperparameters by creating model specification that identifies which hyperparameters we are planning to tune
tune_tree_model <- 
  decision_tree(
    cost_complexity = tune(),
    tree_depth = tune()) %>%
  set_engine("rpart") %>% 
  set_mode("regression")
tune_tree_model

Decision Tree Model Specification (regression)

Main Arguments:
  cost_complexity = tune()
  tree_depth = tune()

Computational engine: rpart 

# Creating a regular grid of values for using some convenience functions for each hyperparameter
tree_grid <- grid_regular(cost_complexity(), tree_depth(), levels = 5)

tree_grid %>% count(tree_depth)

# A tibble: 5 × 2
  tree_depth     n
       <int> <int>
1          1     5
2          4     5
3          8     5
4         11     5
5         15     5

# Tuning the workflow using model specification and recipe and model
tree_wf <- workflow() %>%
  add_model(tune_tree_model) %>%
  add_recipe(recipe_data)

# Tuning using cross-validation and the tune_grid() function
tree_res <- tree_wf %>% 
  tune_grid(resamples = CV_fold_data,
    grid = tree_grid)

! Fold1, Repeat1: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold2, Repeat1: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold3, Repeat1: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold4, Repeat1: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold5, Repeat1: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold1, Repeat2: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold2, Repeat2: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold3, Repeat2: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold4, Repeat2: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold5, Repeat2: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold1, Repeat3: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold2, Repeat3: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold3, Repeat3: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold4, Repeat3: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold5, Repeat3: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold1, Repeat4: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold2, Repeat4: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold3, Repeat4: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold4, Repeat4: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold5, Repeat4: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold1, Repeat5: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold2, Repeat5: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold3, Repeat5: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold4, Repeat5: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

! Fold5, Repeat5: internal:
  There was 1 warning in `dplyr::summarise()`.
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 1`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 4`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 8`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 11`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...
  ℹ In argument: `.estimate = metric_fn(truth = BodyTemp, estimate = .pr...
    = na_rm)`.
  ℹ In group 1: `cost_complexity = 0.1`, `tree_depth = 15`.
  Caused by warning:
  ! A correlation computation is required, but `estimate` is constant an...

MODEL EVALUATION: TREE MODEL

# Plotting the above results
tree_res %>% autoplot()

# Getting the best-fit model
tree_res %>%
  show_best()

Warning: No value of `metric` was given; metric 'rmse' will be used.

# A tibble: 5 × 8
  cost_complexity tree_depth .metric .estimator  mean     n std_err .config     
            <dbl>      <int> <chr>   <chr>      <dbl> <int>   <dbl> <chr>       
1    0.0000000001          1 rmse    standard    1.19    25  0.0181 Preprocesso…
2    0.0000000178          1 rmse    standard    1.19    25  0.0181 Preprocesso…
3    0.00000316            1 rmse    standard    1.19    25  0.0181 Preprocesso…
4    0.000562              1 rmse    standard    1.19    25  0.0181 Preprocesso…
5    0.0000000001          4 rmse    standard    1.20    25  0.0187 Preprocesso…

best_tree <- tree_res %>%
  select_best(metric = "rmse")

# Getting the final workflow
final_wf <- tree_wf %>% 
  finalize_workflow(best_tree)

# Fitting to the training data with the final workflow
final_fit <- final_wf %>%
  fit(train_data)

# Plotting the final fit
rpart.plot(extract_fit_parsnip(final_fit)$fit, roundint = FALSE)

# Predicted outcomes
predicted_fit <- predict(final_fit, train_data)

From the tree model, we got RMSE as 1.19.

MODEL TUNING AND FITTING: LASSO MODEL

# Building the model
model_lasso <- linear_reg(penalty = tune(), mixture = 1) %>% set_engine("glmnet")

# We will be using the recipe (recipe_data) that we created above 
# Creating the workflow
wf_lasso <- workflow() %>% 
  add_model(model_lasso) %>% 
  add_recipe(recipe_data)

# Model tuning using grid
lasso_grid <- tibble(penalty = 10^seq(-4, -1, length.out = 30))
lasso_grid %>% top_n(-5)

Selecting by penalty

# A tibble: 5 × 1
   penalty
     <dbl>
1 0.0001  
2 0.000127
3 0.000161
4 0.000204
5 0.000259

lasso_grid %>% top_n(5)

Selecting by penalty

# A tibble: 5 × 1
  penalty
    <dbl>
1  0.0386
2  0.0489
3  0.0621
4  0.0788
5  0.1   

# Tuning the model using tune_grid() function
res_lasso <- wf_lasso %>% tune_grid(resamples = CV_fold_data, grid = lasso_grid,
    control = control_grid(verbose = FALSE, save_pred = TRUE), metrics = NULL)

res_lasso %>% collect_metrics()

# A tibble: 60 × 7
    penalty .metric .estimator   mean     n std_err .config              
      <dbl> <chr>   <chr>       <dbl> <int>   <dbl> <chr>                
 1 0.0001   rmse    standard   1.18      25 0.0167  Preprocessor1_Model01
 2 0.0001   rsq     standard   0.0819    25 0.00875 Preprocessor1_Model01
 3 0.000127 rmse    standard   1.18      25 0.0167  Preprocessor1_Model02
 4 0.000127 rsq     standard   0.0819    25 0.00875 Preprocessor1_Model02
 5 0.000161 rmse    standard   1.18      25 0.0167  Preprocessor1_Model03
 6 0.000161 rsq     standard   0.0819    25 0.00875 Preprocessor1_Model03
 7 0.000204 rmse    standard   1.18      25 0.0167  Preprocessor1_Model04
 8 0.000204 rsq     standard   0.0819    25 0.00875 Preprocessor1_Model04
 9 0.000259 rmse    standard   1.18      25 0.0167  Preprocessor1_Model05
10 0.000259 rsq     standard   0.0819    25 0.00875 Preprocessor1_Model05
# … with 50 more rows

MODEL EVALUATION: LASSO MODEL

# Plotting the above results
res_lasso %>% autoplot()

# Getting the best-fit model
res_lasso %>%
  show_best()

Warning: No value of `metric` was given; metric 'rmse' will be used.

# A tibble: 5 × 7
  penalty .metric .estimator  mean     n std_err .config              
    <dbl> <chr>   <chr>      <dbl> <int>   <dbl> <chr>                
1  0.0489 rmse    standard    1.15    25  0.0170 Preprocessor1_Model27
2  0.0621 rmse    standard    1.15    25  0.0170 Preprocessor1_Model28
3  0.0386 rmse    standard    1.16    25  0.0170 Preprocessor1_Model26
4  0.0304 rmse    standard    1.16    25  0.0169 Preprocessor1_Model25
5  0.0788 rmse    standard    1.16    25  0.0172 Preprocessor1_Model29

best_lasso <- res_lasso %>%
  select_best(metric = "rmse")

# Getting the final workflow
final_lasso_wf <- wf_lasso %>% 
  finalize_workflow(best_lasso)

# Fitting to the training data with the final workflow
final_lasso_fit <- final_lasso_wf %>% fit(train_data) 

# Plotting the final fit
plot_lasso <- extract_fit_engine(final_lasso_fit)
plot(plot_lasso, "lambda")

# Predicted outcomes
predicted_lasso_fit <- predict(final_lasso_fit, train_data)

From the Lasso model, we got RMSE as 1.16.

MODEL TUNING AND FITTING: RANDOM FOREST MODEL

# Detecting the cores
cores <- parallel::detectCores()
cores

[1] 8

# Building the model
model_randomforest <- rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% 
  set_engine("ranger",importance = "impurity", num.threads = cores) %>%
  set_mode("regression")

# We will be using the recipe (recipe_data) that we created above 
# Creating the workflow
wf_randomforest <- workflow() %>%
  add_model(model_randomforest) %>%
  add_recipe(recipe_data)

# Model tuning
extract_parameter_set_dials(model_randomforest)

Collection of 2 parameters for tuning

 identifier  type    object
       mtry  mtry nparam[?]
      min_n min_n nparam[+]

Model parameters needing finalization:
   # Randomly Selected Predictors ('mtry')

See `?dials::finalize` or `?dials::update.parameters` for more information.

# Tuning the model using tune_grid() function
res_randomforest <- wf_randomforest %>% tune_grid(resamples = CV_fold_data, grid = 25,
    control = control_grid(save_pred = TRUE), metrics = NULL)

i Creating pre-processing data to finalize unknown parameter: mtry

MODEL EVALUATION: RANDOM FOREST MODEL

# Plotting the above results
res_randomforest %>% autoplot()

# Getting the best-fit model
res_randomforest %>%
  show_best()

Warning: No value of `metric` was given; metric 'rmse' will be used.

# A tibble: 5 × 8
   mtry min_n .metric .estimator  mean     n std_err .config              
  <int> <int> <chr>   <chr>      <dbl> <int>   <dbl> <chr>                
1     5    27 rmse    standard    1.16    25  0.0167 Preprocessor1_Model18
2     3    19 rmse    standard    1.17    25  0.0167 Preprocessor1_Model09
3     8    24 rmse    standard    1.17    25  0.0168 Preprocessor1_Model04
4    11    36 rmse    standard    1.17    25  0.0168 Preprocessor1_Model13
5     6    15 rmse    standard    1.17    25  0.0165 Preprocessor1_Model22

best_randomforest <- res_randomforest %>%
  select_best(metric = "rmse")

# Getting the final workflow
final_randomforest_wf <- wf_randomforest %>% 
  finalize_workflow(best_randomforest)

# Fitting to the training data with the final workflow
final_randomforest_fit <- final_randomforest_wf %>% fit(train_data) 
final_randomforest_fit %>% extract_fit_parsnip() %>% vip(num_features = 28)

# Plotting the final fit
plot_randomforest <- extract_fit_engine(final_randomforest_fit)
vip(plot_randomforest)

# Predicted outcomes
predicted_randomforest_fit <- predict(final_randomforest_fit, train_data)

From the random forest model, we got RMSE as 1.17.

Based on the above results, lasso model performed the best as it had the lowest RMSE compared to others. Hence, lasso model was selected.

FINAL EVALUATION

# Fitting the final lasso model on split data
final_lasso_test <- final_lasso_wf %>% last_fit(data_split) 

final_lasso_test %>% collect_metrics()

# A tibble: 2 × 4
  .metric .estimator .estimate .config             
  <chr>   <chr>          <dbl> <chr>               
1 rmse    standard      1.16   Preprocessor1_Model1
2 rsq     standard      0.0299 Preprocessor1_Model1

From the above final model, we got RMSE as 1.16.