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here() starts at /Users/deannalanier/Desktop/All_Classes_UGA/2023Spr_Classes/MADA/deannalanier-MADA-portfolio
Attaching package: 'vip'
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Loading required package: rpart
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• Learn how to get started at https://www.tidymodels.org/start/
Loading required package: Matrix
Attaching package: 'Matrix'
The following objects are masked from 'package:tidyr':
expand, pack, unpack
Loaded glmnet 4.1-7Flu Anlaysis - Machine Learning
Focused on single outcome, the continuous, numerical value of Body Temperature.
Load Libraries
Load the data
#path to clean data
data = readRDS(here("fluanalysis", "data", "cleandata2.rds")) #load RDS fileCheck the data
#check the data to make sure it has loaded properly
head(data) SwollenLymphNodes ChestCongestion ChillsSweats NasalCongestion Sneeze Fatigue
1 Yes No No No No Yes
2 Yes Yes No Yes No Yes
3 Yes Yes Yes Yes Yes Yes
4 Yes Yes Yes Yes Yes Yes
5 Yes No Yes No No Yes
6 No No Yes No Yes Yes
SubjectiveFever Headache Weakness CoughIntensity Myalgia RunnyNose AbPain
1 Yes Yes Mild Severe Mild No No
2 Yes Yes Severe Severe Severe No No
3 Yes Yes Severe Mild Severe Yes Yes
4 Yes Yes Severe Moderate Severe Yes No
5 Yes Yes Moderate None Mild No No
6 Yes Yes Moderate Moderate Moderate No No
ChestPain Diarrhea EyePn Insomnia ItchyEye Nausea EarPn Pharyngitis
1 No No No No No No No Yes
2 No No No No No No Yes Yes
3 Yes No No Yes No Yes No Yes
4 No No No Yes No Yes Yes Yes
5 No No Yes Yes No Yes No Yes
6 Yes Yes No No No Yes No Yes
Breathless ToothPn Vomit Wheeze BodyTemp
1 No No No No 98.3
2 No No No No 100.4
3 Yes Yes No No 100.8
4 No No No Yes 98.8
5 No No No No 100.5
6 Yes No No Yes 98.4#Setup
- Set random seed to 123
- Split the dataset into 70% training 30% testing. Use BodyTemp as stratification
- Do 5-fold cross-validation, 5 times repeated.
- Create a recipe for the data and fitting
1. Set random seed to 123
# set seed
set.seed(123)2. Split the data
## Split the data into test and training
data_split = initial_split(data,strata = BodyTemp, prop = 7/10)
#create training and test
data_train=training(data_split)
data_test=testing(data_split)3. Cross validation
# 5 fold cross-validation 5 times repeated 5x5
# stratify on Body Temp
# use vfold_cv to create a resample object for the training data
#CV on training data
fold_train = vfold_cv(data_train, v = 5, repeats = 5, strata = BodyTemp)
fold_train# 5-fold cross-validation repeated 5 times using stratification
# A tibble: 25 × 3
splits id id2
<list> <chr> <chr>
1 <split [405/103]> Repeat1 Fold1
2 <split [405/103]> Repeat1 Fold2
3 <split [406/102]> Repeat1 Fold3
4 <split [408/100]> Repeat1 Fold4
5 <split [408/100]> Repeat1 Fold5
6 <split [405/103]> Repeat2 Fold1
7 <split [405/103]> Repeat2 Fold2
8 <split [406/102]> Repeat2 Fold3
9 <split [408/100]> Repeat2 Fold4
10 <split [408/100]> Repeat2 Fold5
# … with 15 more rows#CV on test data
fold_test = vfold_cv(data_test, v = 5, repeats = 5, strata = BodyTemp)
fold_test# 5-fold cross-validation repeated 5 times using stratification
# A tibble: 25 × 3
splits id id2
<list> <chr> <chr>
1 <split [175/47]> Repeat1 Fold1
2 <split [176/46]> Repeat1 Fold2
3 <split [179/43]> Repeat1 Fold3
4 <split [179/43]> Repeat1 Fold4
5 <split [179/43]> Repeat1 Fold5
6 <split [175/47]> Repeat2 Fold1
7 <split [176/46]> Repeat2 Fold2
8 <split [179/43]> Repeat2 Fold3
9 <split [179/43]> Repeat2 Fold4
10 <split [179/43]> Repeat2 Fold5
# … with 15 more rows4. Create a recipe for the data and fitting
# categorical variables as dummy variables
#pick all nominal predictor variables
data_recipe = recipe(BodyTemp ~ ., data = data_train) %>%
step_dummy(all_nominal(), -all_outcomes()) Null Model Performance
compute the performance of a null model
# compute the performance of a null model
nullmodel = null_model() %>%
set_engine("parsnip") %>%
set_mode("regression")Compute the RSME for training and test data for the model
## training data
#recipe
nullr_train = recipe(BodyTemp ~ 1, data = data_train)
#workflow
nullw_train = workflow() %>%
add_model(nullmodel) %>%
add_recipe(nullr_train)
#fit
nullf_train = fit_resamples(nullw_train, resamples = fold_train)! 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...#null model recipe with testing data
#recipe
nullr_test = recipe(BodyTemp ~ 1, data = data_test)
#workflow
nullw_test = workflow() %>%
add_model(nullmodel) %>%
add_recipe(nullr_test)
#fit
nullf_test = fit_resamples(nullw_test, resamples = fold_test)! 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...Null Model Metrics
#RMSE and RSQ for training set
nullf_train %>% collect_metrics()# 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#RMSE and RSQ for test set
nullf_test %>% collect_metrics()# A tibble: 2 × 6
.metric .estimator mean n std_err .config
<chr> <chr> <dbl> <int> <dbl> <chr>
1 rmse standard 1.16 25 0.0285 Preprocessor1_Model1
2 rsq standard NaN 0 NA Preprocessor1_Model1Model Tuning and Fitting
- Tree
- LASSO
- random forest
The steps (blocks of code) included are 1) model specification, 2) workflow definition, 3) tuning grip specification and 4) tuning using cross-validation and the tune_grip function
Tree
Model specification
tree_spec = decision_tree(cost_complexity = tune(),tree_depth = tune())%>%
set_engine("rpart")%>%
set_mode("regression")
tree_specDecision Tree Model Specification (regression)
Main Arguments:
cost_complexity = tune()
tree_depth = tune()
Computational engine: rpart Workflow definition
#create workflow
tree_workflow = workflow()%>%
add_model(tree_spec)%>%
add_recipe(data_recipe) #recipe created in step 4 of the setuptuning grid specification
tree_grid = grid_regular(cost_complexity(),
tree_depth(),
levels = 5)
tree_grid# A tibble: 25 × 2
cost_complexity tree_depth
<dbl> <int>
1 0.0000000001 1
2 0.0000000178 1
3 0.00000316 1
4 0.000562 1
5 0.1 1
6 0.0000000001 4
7 0.0000000178 4
8 0.00000316 4
9 0.000562 4
10 0.1 4
# … with 15 more rows#depth
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 5tuning using cross validation
tree_cv = tree_workflow %>%
tune_grid(
resamples = fold_train,
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...tree_cv# Tuning results
# 5-fold cross-validation repeated 5 times using stratification
# A tibble: 25 × 5
splits id id2 .metrics .notes
<list> <chr> <chr> <list> <list>
1 <split [405/103]> Repeat1 Fold1 <tibble [50 × 6]> <tibble [1 × 3]>
2 <split [405/103]> Repeat1 Fold2 <tibble [50 × 6]> <tibble [1 × 3]>
3 <split [406/102]> Repeat1 Fold3 <tibble [50 × 6]> <tibble [1 × 3]>
4 <split [408/100]> Repeat1 Fold4 <tibble [50 × 6]> <tibble [1 × 3]>
5 <split [408/100]> Repeat1 Fold5 <tibble [50 × 6]> <tibble [1 × 3]>
6 <split [405/103]> Repeat2 Fold1 <tibble [50 × 6]> <tibble [1 × 3]>
7 <split [405/103]> Repeat2 Fold2 <tibble [50 × 6]> <tibble [1 × 3]>
8 <split [406/102]> Repeat2 Fold3 <tibble [50 × 6]> <tibble [1 × 3]>
9 <split [408/100]> Repeat2 Fold4 <tibble [50 × 6]> <tibble [1 × 3]>
10 <split [408/100]> Repeat2 Fold5 <tibble [50 × 6]> <tibble [1 × 3]>
# … with 15 more rows
There were issues with some computations:
- Warning(s) x25: There was 1 warning in `dplyr::summarise()`. ℹ In argument: `.est...
Run `show_notes(.Last.tune.result)` for more information.#use collect metrics to give tibble with the results from the tuning
tree_cv %>%
collect_metrics()# A tibble: 50 × 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 Prepro…
2 0.0000000001 1 rsq standard 0.0361 25 0.00422 Prepro…
3 0.0000000178 1 rmse standard 1.19 25 0.0181 Prepro…
4 0.0000000178 1 rsq standard 0.0361 25 0.00422 Prepro…
5 0.00000316 1 rmse standard 1.19 25 0.0181 Prepro…
6 0.00000316 1 rsq standard 0.0361 25 0.00422 Prepro…
7 0.000562 1 rmse standard 1.19 25 0.0181 Prepro…
8 0.000562 1 rsq standard 0.0361 25 0.00422 Prepro…
9 0.1 1 rmse standard 1.21 25 0.0177 Prepro…
10 0.1 1 rsq standard NaN 0 NA Prepro…
# … with 40 more rowsModel Evaluation
Look at diagnostics using autoplot().
tree_cv %>% autoplot()
Get the model that the tuning process has determined is the best using select_best() and finalize_workflow().
tree_cv %>%
show_best(metric = "rmse")# 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 0.0182 Preprocesso…tree_best = tree_cv %>%
select_best(metric = "rmse")
tree_best# A tibble: 1 × 3
cost_complexity tree_depth .config
<dbl> <int> <chr>
1 0.0000000001 1 Preprocessor1_Model01Finalize workflow with the fit() function
tree_f_workflow = tree_workflow %>%
finalize_workflow(tree_best)
tree_f_fit = tree_f_workflow %>% fit(data=data_train)
tree_f_fit══ Workflow [trained] ══════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: decision_tree()
── Preprocessor ────────────────────────────────────────────────────────────────
1 Recipe Step
• step_dummy()
── Model ───────────────────────────────────────────────────────────────────────
n= 508
node), split, n, deviance, yval
* denotes terminal node
1) root 508 742.9363 98.93642
2) Sneeze_Yes>=0.5 280 259.6477 98.69107 *
3) Sneeze_Yes< 0.5 228 445.7356 99.23772 *#plot tree
rpart.plot(extract_fit_parsnip(tree_f_fit)$fit)Warning: Cannot retrieve the data used to build the model (model.frame: object '..y' not found).
To silence this warning:
Call rpart.plot with roundint=FALSE,
or rebuild the rpart model with model=TRUE.
evaluate the final fit
#predicted and residuals
tree_residuals = tree_f_fit %>%
augment(data_train) %>% #use augment() to make predictions from train data
select(c(.pred, BodyTemp)) %>%
mutate(.resid = BodyTemp - .pred) #calculate residuals and make new row.
tree_residuals# A tibble: 508 × 3
.pred BodyTemp .resid
<dbl> <dbl> <dbl>
1 99.2 97.8 -1.44
2 99.2 98.1 -1.14
3 98.7 98.1 -0.591
4 98.7 98.2 -0.491
5 98.7 97.8 -0.891
6 98.7 98.2 -0.491
7 98.7 98.1 -0.591
8 99.2 98 -1.24
9 99.2 97.7 -1.54
10 99.2 98.2 -1.04
# … with 498 more rows# Plot predicted values vs actual values
plot_tree_predicted = tree_residuals %>%
ggplot(aes(x = BodyTemp, y = .pred)) +
geom_point() +
labs(title = "Predicted outcomes vs Actual Outcomes",
x = "Body Temp Actual",
y = "Body Temp Prediction")
plot_tree_predicted
# Plot predicted values vs residuals
plot_tree_residual = ggplot(tree_residuals,
aes(y = .resid,
x = .pred)) +
geom_point() +
labs(title = "Prediction Outcomes vs Residuals: Decision Tree",
x = "Body Temp Prediction",
y = "Residuals")
plot(plot_tree_residual) 
performance
tree_cv %>%
show_best(metric = "rmse", n=1)# A tibble: 1 × 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…LASSO
Model specification
#mixture = 1 -> glmnet will remove irrelevant predictors
lasso_mod = linear_reg(penalty = tune(), mixture = 1) %>%
set_engine("glmnet")Workflow definition
lasso_workflow = workflow() %>%
add_model(lasso_mod) %>%
add_recipe(data_recipe)tuning grid specification
lasso_grid = tibble(penalty = 10^seq(-4, -1, length.out = 30))
lasso_grid %>% top_n(-6)Selecting by penalty# A tibble: 6 × 1
penalty
<dbl>
1 0.0001
2 0.000127
3 0.000161
4 0.000204
5 0.000259
6 0.000329lasso_grid %>% top_n(6)Selecting by penalty# A tibble: 6 × 1
penalty
<dbl>
1 0.0304
2 0.0386
3 0.0489
4 0.0621
5 0.0788
6 0.1 tuning using cross validation
lasso_cv = lasso_workflow %>%
tune_grid(resamples = fold_train,
grid = lasso_grid,
control = control_grid(verbose = FALSE, save_pred = TRUE),
metrics = metric_set(rmse))
lasso_cv%>% collect_metrics()# A tibble: 30 × 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 0.000127 rmse standard 1.18 25 0.0167 Preprocessor1_Model02
3 0.000161 rmse standard 1.18 25 0.0167 Preprocessor1_Model03
4 0.000204 rmse standard 1.18 25 0.0167 Preprocessor1_Model04
5 0.000259 rmse standard 1.18 25 0.0167 Preprocessor1_Model05
6 0.000329 rmse standard 1.18 25 0.0167 Preprocessor1_Model06
7 0.000418 rmse standard 1.18 25 0.0167 Preprocessor1_Model07
8 0.000530 rmse standard 1.18 25 0.0167 Preprocessor1_Model08
9 0.000672 rmse standard 1.18 25 0.0167 Preprocessor1_Model09
10 0.000853 rmse standard 1.18 25 0.0167 Preprocessor1_Model10
# … with 20 more rowsModel Evaluation
Look at diagnostics using autoplot().
lasso_cv %>% autoplot()
Get the model that the tuning process has determined is the best using select_best() and finalize_workflow().
lasso_cv %>%
show_best(metric = "rmse")# A tibble: 5 × 7
penalty .metric .estimator mean n std_err .config
<dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
1 0.0621 rmse standard 1.15 25 0.0169 Preprocessor1_Model28
2 0.0489 rmse standard 1.15 25 0.0169 Preprocessor1_Model27
3 0.0386 rmse standard 1.15 25 0.0169 Preprocessor1_Model26
4 0.0788 rmse standard 1.16 25 0.0171 Preprocessor1_Model29
5 0.0304 rmse standard 1.16 25 0.0169 Preprocessor1_Model25lasso_best = lasso_cv %>%
select_best(metric = "rmse")
lasso_best# A tibble: 1 × 2
penalty .config
<dbl> <chr>
1 0.0621 Preprocessor1_Model28Finalize workflow with the fit() function
lasso_f_workflow = lasso_workflow %>%
finalize_workflow(lasso_best)
lasso_f_fit = lasso_f_workflow %>% fit(data=data_train)
lasso_f_fit══ Workflow [trained] ══════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: linear_reg()
── Preprocessor ────────────────────────────────────────────────────────────────
1 Recipe Step
• step_dummy()
── Model ───────────────────────────────────────────────────────────────────────
Call: glmnet::glmnet(x = maybe_matrix(x), y = y, family = "gaussian", alpha = ~1)
Df %Dev Lambda
1 0 0.00 0.271900
2 2 1.24 0.247700
3 2 2.67 0.225700
4 2 3.86 0.205700
5 2 4.85 0.187400
6 2 5.67 0.170800
7 2 6.35 0.155600
8 2 6.91 0.141800
9 5 7.57 0.129200
10 5 8.27 0.117700
11 8 9.06 0.107200
12 8 9.81 0.097710
13 9 10.44 0.089030
14 9 11.09 0.081120
15 9 11.63 0.073920
16 10 12.12 0.067350
17 10 12.56 0.061370
18 12 13.00 0.055910
19 14 13.45 0.050950
20 16 13.85 0.046420
21 19 14.24 0.042300
22 19 14.59 0.038540
23 19 14.87 0.035120
24 22 15.17 0.032000
25 22 15.44 0.029150
26 22 15.67 0.026560
27 22 15.85 0.024200
28 23 16.01 0.022050
29 24 16.15 0.020090
30 25 16.28 0.018310
31 25 16.39 0.016680
32 25 16.49 0.015200
33 25 16.56 0.013850
34 26 16.63 0.012620
35 27 16.69 0.011500
36 27 16.73 0.010480
37 27 16.77 0.009547
38 27 16.81 0.008698
39 28 16.84 0.007926
40 29 16.86 0.007222
41 29 16.88 0.006580
42 29 16.90 0.005995
43 29 16.91 0.005463
44 29 16.92 0.004978
45 30 16.93 0.004535
46 30 16.94 0.004132
...
and 22 more lines.#plot for how the number of predictors included in the LASSO model changes with the tuning parameter
x = lasso_f_fit$fit$fit$fit
plot(x, "lambda")
evaluate the final fit (repeat)
lasso_residual = lasso_f_fit %>%
augment(data_train) %>%
select(c(.pred, BodyTemp)) %>%
mutate(resid = BodyTemp - .pred)
lasso_residual# A tibble: 508 × 3
.pred BodyTemp resid
<dbl> <dbl> <dbl>
1 98.8 97.8 -0.950
2 98.8 98.1 -0.719
3 98.5 98.1 -0.360
4 98.8 98.2 -0.606
5 98.7 97.8 -0.907
6 98.7 98.2 -0.523
7 98.4 98.1 -0.257
8 99.3 98 -1.26
9 98.9 97.7 -1.24
10 99.0 98.2 -0.769
# … with 498 more rows# Plot predicted values vs actual values
plot_lasso_predicted = lasso_residual %>%
ggplot(aes(x = BodyTemp, y = .pred)) +
geom_point() +
labs(title = "Predicted Outcomes vs Actual Outcomes",
x = "Body Temp Actual",
y = "Body Temp Prediction")
plot_lasso_predicted
# Plot predicted values vs residuals
plot_lasso_residual = lasso_residual %>%
ggplot(aes(x = resid, y = .pred)) +
geom_point() +
labs(title = "Predictions vs Residual",
x = "Residuals",
y = "Body Temp Prediction")
plot_lasso_residual
performance
lasso_cv %>%
show_best(metric = "rmse", n=1)# A tibble: 1 × 7
penalty .metric .estimator mean n std_err .config
<dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
1 0.0621 rmse standard 1.15 25 0.0169 Preprocessor1_Model28Random Forest
Model specification
cores = parallel::detectCores()
cores[1] 10randomfor_model <-
rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>%
set_engine("ranger", num.threads = cores) %>%
set_mode("regression")Workflow definition
randomfor_workflow <-
workflow() %>%
add_model(randomfor_model) %>%
add_recipe(data_recipe)tuning grid specification
randomfor_modelRandom Forest Model Specification (regression)
Main Arguments:
mtry = tune()
trees = 1000
min_n = tune()
Engine-Specific Arguments:
num.threads = cores
Computational engine: ranger extract_parameter_set_dials(randomfor_model)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 using cross validation
randomfor_cv = randomfor_workflow %>%
tune_grid(resamples = fold_train,
grid = 25,
control = control_grid(save_pred = TRUE),
metrics = metric_set(rmse))i Creating pre-processing data to finalize unknown parameter: mtryrandomfor_cv %>%
collect_metrics()# A tibble: 25 × 8
mtry min_n .metric .estimator mean n std_err .config
<int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
1 13 13 rmse standard 1.19 25 0.0166 Preprocessor1_Model01
2 5 36 rmse standard 1.17 25 0.0165 Preprocessor1_Model02
3 16 28 rmse standard 1.18 25 0.0166 Preprocessor1_Model03
4 30 40 rmse standard 1.18 25 0.0168 Preprocessor1_Model04
5 11 30 rmse standard 1.17 25 0.0164 Preprocessor1_Model05
6 7 26 rmse standard 1.17 25 0.0166 Preprocessor1_Model06
7 22 26 rmse standard 1.19 25 0.0165 Preprocessor1_Model07
8 10 11 rmse standard 1.19 25 0.0166 Preprocessor1_Model08
9 7 2 rmse standard 1.20 25 0.0161 Preprocessor1_Model09
10 9 6 rmse standard 1.19 25 0.0159 Preprocessor1_Model10
# … with 15 more rowsModel Evaluation
Look at diagnostics using autoplot().
autoplot(randomfor_cv)
Get the model that the tuning process has determined is the best using select_best() and finalize_workflow().
randomfor_cv %>%
show_best(metric = "rmse")# A tibble: 5 × 8
mtry min_n .metric .estimator mean n std_err .config
<int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
1 6 33 rmse standard 1.17 25 0.0166 Preprocessor1_Model23
2 5 36 rmse standard 1.17 25 0.0165 Preprocessor1_Model02
3 7 26 rmse standard 1.17 25 0.0166 Preprocessor1_Model06
4 2 12 rmse standard 1.17 25 0.0167 Preprocessor1_Model25
5 2 4 rmse standard 1.17 25 0.0166 Preprocessor1_Model19randomfor_best = randomfor_cv %>%
select_best(metric = "rmse")
randomfor_best# A tibble: 1 × 3
mtry min_n .config
<int> <int> <chr>
1 6 33 Preprocessor1_Model23finalize workflow
randomfor_f_workflow = randomfor_workflow %>%
finalize_workflow(randomfor_best)
randomfor_f_fit = randomfor_f_workflow %>% fit(data=data_train)
randomfor_f_fit══ Workflow [trained] ══════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: rand_forest()
── Preprocessor ────────────────────────────────────────────────────────────────
1 Recipe Step
• step_dummy()
── Model ───────────────────────────────────────────────────────────────────────
Ranger result
Call:
ranger::ranger(x = maybe_data_frame(x), y = y, mtry = min_cols(~6L, x), num.trees = ~1000, min.node.size = min_rows(~33L, x), num.threads = ~cores, verbose = FALSE, seed = sample.int(10^5, 1))
Type: Regression
Number of trees: 1000
Sample size: 508
Number of independent variables: 31
Mtry: 6
Target node size: 33
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 1.373913
R squared (OOB): 0.06240402 evaluate the final fit (repeat)
# get predicted and residual values in one dataset
randomfor_residual = randomfor_f_fit %>%
augment(data_train) %>%
select(c(.pred, BodyTemp)) %>%
mutate(resid = BodyTemp - .pred)
randomfor_residual# A tibble: 508 × 3
.pred BodyTemp resid
<dbl> <dbl> <dbl>
1 98.7 97.8 -0.901
2 98.5 98.1 -0.448
3 98.7 98.1 -0.554
4 98.7 98.2 -0.523
5 98.8 97.8 -0.990
6 98.5 98.2 -0.306
7 98.3 98.1 -0.206
8 99.1 98 -1.12
9 98.8 97.7 -1.09
10 98.9 98.2 -0.667
# … with 498 more rows# Plot actual values vs predicted values
plot_randomfor_predicted = randomfor_residual %>%
ggplot(aes(x = BodyTemp, y = .pred)) +
geom_point() +
labs(title = "Predictions vs Actual",
x = "Body Temp Actual",
y = "Body Temp Prediction")
plot_randomfor_predicted
# Plot predicted values vs residuals
plot_randomfor_residual = randomfor_residual %>%
ggplot(aes(x = resid, y = .pred)) +
geom_point() +
labs(title = "Predictions vs Residual",
x = "Residual",
y = "Body Temp Prediction")
plot_randomfor_residual
performance
randomfor_cv %>%
show_best(metric = "rmse", n=1)# A tibble: 1 × 8
mtry min_n .metric .estimator mean n std_err .config
<int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
1 6 33 rmse standard 1.17 25 0.0166 Preprocessor1_Model23Final Evaluation
All models have similar performance based on RMSE. When using the plots for visual analysis of the performance, you can see there is more of a visual relationship using LASSO and Random Forest but not the tree model. I selected the Lasso model because when accounting for more significant figures, it is lower.
#fit the test data once
lasso_lastfit = lasso_f_workflow %>%
last_fit(data_split)
lasso_lastfit %>% collect_metrics()# A tibble: 2 × 4
.metric .estimator .estimate .config
<chr> <chr> <dbl> <chr>
1 rmse standard 1.15 Preprocessor1_Model1
2 rsq standard 0.0291 Preprocessor1_Model1#compare with the null
nullf_test %>% collect_metrics()# A tibble: 2 × 6
.metric .estimator mean n std_err .config
<chr> <chr> <dbl> <int> <dbl> <chr>
1 rmse standard 1.16 25 0.0285 Preprocessor1_Model1
2 rsq standard NaN 0 NA Preprocessor1_Model1#lasso_last_residual = lasso_lastfit %>%
# augment(data_test) %>%
# select(c(.pred, BodyTemp)) %>%
# mutate(resid = BodyTemp - .pred)
#lasso_last_residualConclusion
The Data above show that the model with all the possible predictors is the best model to fit the data as it minimizes the Root Mean Squared Errors (RMSE) of the data.