Import data

library(tidyverse)
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library(correlationfunnel)
## ══ Using correlationfunnel? ════════════════════════════════════════════════════
## You might also be interested in applied data science training for business.
## </> Learn more at - www.business-science.io </>
library(tidymodels) #for building models
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library(textrecipes) # For processing string variable
library(tidytext)
library(ggrepel)
## Warning: package 'ggrepel' was built under R version 4.3.3
members <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-22/members.csv')
## Rows: 76519 Columns: 21
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): expedition_id, member_id, peak_id, peak_name, season, sex, citizen...
## dbl  (5): year, age, highpoint_metres, death_height_metres, injury_height_me...
## lgl  (6): hired, success, solo, oxygen_used, died, injured
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Clean data

skimr::skim(members)
Data summary
Name members
Number of rows 76519
Number of columns 21
_______________________
Column type frequency:
character 10
logical 6
numeric 5
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
expedition_id 0 1.00 9 9 0 10350 0
member_id 0 1.00 12 12 0 76518 0
peak_id 0 1.00 4 4 0 391 0
peak_name 15 1.00 4 25 0 390 0
season 0 1.00 6 7 0 5 0
sex 2 1.00 1 1 0 2 0
citizenship 10 1.00 2 23 0 212 0
expedition_role 21 1.00 4 25 0 524 0
death_cause 75413 0.01 3 27 0 12 0
injury_type 74807 0.02 3 27 0 11 0

Variable type: logical

skim_variable n_missing complete_rate mean count
hired 0 1 0.21 FAL: 60788, TRU: 15731
success 0 1 0.38 FAL: 47320, TRU: 29199
solo 0 1 0.00 FAL: 76398, TRU: 121
oxygen_used 0 1 0.24 FAL: 58286, TRU: 18233
died 0 1 0.01 FAL: 75413, TRU: 1106
injured 0 1 0.02 FAL: 74806, TRU: 1713

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
year 0 1.00 2000.36 14.78 1905 1991 2004 2012 2019 ▁▁▁▃▇
age 3497 0.95 37.33 10.40 7 29 36 44 85 ▁▇▅▁▁
highpoint_metres 21833 0.71 7470.68 1040.06 3800 6700 7400 8400 8850 ▁▁▆▃▇
death_height_metres 75451 0.01 6592.85 1308.19 400 5800 6600 7550 8830 ▁▁▂▇▆
injury_height_metres 75510 0.01 7049.91 1214.24 400 6200 7100 8000 8880 ▁▁▂▇▇

Explore data

members1 <- members %>%
    # Treat missing values
    select(-death_height_metres, -injury_height_metres, -death_cause, -injury_type, -peak_id) %>%
    filter(!is.na(age)) %>%
    filter(!is.na(highpoint_metres)) %>%
    distinct(member_id, .keep_all = TRUE)

members1 %>% filter(duplicated(member_id))
## # A tibble: 0 × 16
## # ℹ 16 variables: expedition_id <chr>, member_id <chr>, peak_name <chr>,
## #   year <dbl>, season <chr>, sex <chr>, age <dbl>, citizenship <chr>,
## #   expedition_role <chr>, hired <lgl>, highpoint_metres <dbl>, success <lgl>,
## #   solo <lgl>, oxygen_used <lgl>, died <lgl>, injured <lgl>
factors_vec1 <- members1 %>% select(hired, success, solo, oxygen_used, died, injured) %>% names()

members1_clean <- members1 %>%
    # Address factors imported as numeric
    mutate(across(all_of(factors_vec1), as.factor)) %>%
    
    # Recode Attrition
    mutate(died = if_else(died == "TRUE", "Died", died)) %>%
    
    # Convert character to factor
    mutate(across(where(is.character), factor))
members1 %>% count(died)
## # A tibble: 2 × 2
##   died      n
##   <lgl> <int>
## 1 FALSE 51662
## 2 TRUE    744
members1 %>%
    ggplot(aes(died)) +
    geom_bar()

Died vs. age

members1 %>%
    ggplot(aes(died, age)) +
    geom_boxplot()

Correlation plot

# Step 1: binarze 
data_binarized3 <- members1_clean %>%
    select(-expedition_id, -member_id) %>%
    na.omit() %>%
        binarize()

data_binarized3 %>% glimpse()
## Rows: 52,383
## Columns: 67
## $ peak_name__Ama_Dablam          <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ peak_name__Annapurna_I         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Baruntse            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Cho_Oyu             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Dhaulagiri_I        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Everest             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Himlung_Himal       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Kangchenjunga       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Lhotse              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Makalu              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Manaslu             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Pumori              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `peak_name__-OTHER`            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `year__-Inf_1997`              <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ year__1997_2007                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ year__2007_2012                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ year__2012_Inf                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ season__Autumn                 <dbl> 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ season__Spring                 <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ season__Winter                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `season__-OTHER`               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ sex__F                         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ sex__M                         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ `age__-Inf_29`                 <dbl> 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, …
## $ age__29_36                     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, …
## $ age__36_43                     <dbl> 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, …
## $ age__43_Inf                    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Australia         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Austria           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Canada            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__China             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__France            <dbl> 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Germany           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__India             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Italy             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Japan             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Nepal             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__New_Zealand       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Poland            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Russia            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__S_Korea           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Spain             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Switzerland       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__UK                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__USA               <dbl> 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, …
## $ `citizenship__-OTHER`          <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
## $ expedition_role__Climber       <dbl> 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, …
## $ expedition_role__Deputy_Leader <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `expedition_role__H-A_Worker`  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ expedition_role__Leader        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `expedition_role__-OTHER`      <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, …
## $ hired__FALSE                   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ hired__TRUE                    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `highpoint_metres__-Inf_6750`  <dbl> 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ highpoint_metres__6750_7400    <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ highpoint_metres__7400_8450    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ highpoint_metres__8450_Inf     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ success__FALSE                 <dbl> 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ success__TRUE                  <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ solo__FALSE                    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ `solo__-OTHER`                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ oxygen_used__FALSE             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ oxygen_used__TRUE              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ died__Died                     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ died__FALSE                    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ injured__FALSE                 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ injured__TRUE                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
# Step 2: correlation
data_correlation2 <- data_binarized3 %>%
    correlate(died__Died)
## Warning: correlate(): [Data Imbalance Detected] Consider sampling to balance the classes more than 5%
##   Column with imbalance: died__Died
data_correlation2
## # A tibble: 67 × 3
##    feature         bin          correlation
##    <fct>           <chr>              <dbl>
##  1 died            Died              1     
##  2 died            FALSE            -1     
##  3 year            -Inf_1997         0.0843
##  4 success         FALSE             0.0562
##  5 success         TRUE             -0.0562
##  6 peak_name       Annapurna_I       0.0431
##  7 year            2012_Inf         -0.0330
##  8 peak_name       Ama_Dablam       -0.0323
##  9 peak_name       Dhaulagiri_I      0.0315
## 10 expedition_role H-A_Worker       -0.0309
## # ℹ 57 more rows
# Step 3: plot
data_correlation2 %>% 
    correlationfunnel::plot_correlation_funnel() 
## Warning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Model bulidnig

Split data

library(tidymodels)

set.seed(1234)
#members1_clean <- members1_clean #%>% sample_n(100)
members1_clean <- members1_clean #%>% 
    #group_by(died) %>% 
    #sample_n(50) %>% 
    #ungroup()

members_split <- initial_split(members1_clean, strata = died)
members_train <- training(members_split)
members_test <- testing(members_split)

members_cv <- rsample::vfold_cv(members_train, strata = died)
members_cv
## #  10-fold cross-validation using stratification 
## # A tibble: 10 × 2
##    splits               id    
##    <list>               <chr> 
##  1 <split [35373/3931]> Fold01
##  2 <split [35373/3931]> Fold02
##  3 <split [35373/3931]> Fold03
##  4 <split [35373/3931]> Fold04
##  5 <split [35374/3930]> Fold05
##  6 <split [35374/3930]> Fold06
##  7 <split [35374/3930]> Fold07
##  8 <split [35374/3930]> Fold08
##  9 <split [35374/3930]> Fold09
## 10 <split [35374/3930]> Fold10

Preprocess data

library(themis)

xgboost_rec1 <- recipes::recipe(died ~ ., data = members_train) %>%
    update_role(member_id, new_role = "ID") %>%
    step_impute_knn(all_predictors()) %>%
    step_other(citizenship, peak_name, expedition_id, expedition_role, threshold = 0.1) %>%
    step_dummy(all_nominal_predictors(), one_hot = TRUE) %>%
    step_smote(died)
   
xgboost_rec1 %>% prep() %>% juice() %>% glimpse() 
## Rows: 77,492
## Columns: 33
## $ member_id                  <fct> AMAD78301-02, AMAD78301-04, AMAD78301-08, A…
## $ year                       <dbl> 1978, 1978, 1978, 1979, 1979, 1979, 1979, 1…
## $ age                        <dbl> 41, 40, 29, 37, 23, 42, 30, 28, 33, 29, 26,…
## $ highpoint_metres           <dbl> 6000, 6000, 6000, 6814, 6814, 6814, 6814, 6…
## $ died                       <fct> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F…
## $ expedition_id_HIML13308    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ expedition_id_other        <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ peak_name_Ama.Dablam       <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ peak_name_Cho.Oyu          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ peak_name_Everest          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ peak_name_other            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ season_Autumn              <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1…
## $ season_Spring              <dbl> 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0…
## $ season_Summer              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ season_Winter              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ sex_F                      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ sex_M                      <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ citizenship_Nepal          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ citizenship_other          <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ expedition_role_Climber    <dbl> 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1…
## $ expedition_role_H.A.Worker <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ expedition_role_Leader     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ expedition_role_other      <dbl> 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0…
## $ hired_FALSE.               <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ hired_TRUE.                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ success_FALSE.             <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ success_TRUE.              <dbl> 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ solo_FALSE.                <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ solo_TRUE.                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ oxygen_used_FALSE.         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ oxygen_used_TRUE.          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ injured_FALSE.             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ injured_TRUE.              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…

Specify model

library(usemodels)
usemodels::use_xgboost(died ~ ., data = members_train)
## xgboost_recipe <- 
##   recipe(formula = died ~ ., data = members_train) %>% 
##   step_zv(all_predictors()) 
## 
## xgboost_spec <- 
##   boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), 
##     loss_reduction = tune(), sample_size = tune()) %>% 
##   set_mode("classification") %>% 
##   set_engine("xgboost") 
## 
## xgboost_workflow <- 
##   workflow() %>% 
##   add_recipe(xgboost_recipe) %>% 
##   add_model(xgboost_spec) 
## 
## set.seed(33141)
## xgboost_tune <-
##   tune_grid(xgboost_workflow, resamples = stop("add your rsample object"), grid = stop("add number of candidate points"))
xgboost_spec1 <- 
  boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), 
    loss_reduction = tune(), sample_size = tune()) %>% 
  set_mode("classification") %>% 
  set_engine("xgboost") 

xgboost_workflow1 <- 
  workflow() %>% 
  add_recipe(xgboost_rec1) %>% 
  add_model(xgboost_spec1)

Tune hyperparameter

doParallel::registerDoParallel()

set.seed(20020)
xgboost_tune <-
  tune_grid(xgboost_workflow1, 
            resamples = members_cv,
            grid = 5,
            control = control_grid(save_pred = TRUE))
## Warning: package 'xgboost' was built under R version 4.3.3

Model evaluation

Identify optimal values for hyperperamters

collect_metrics(xgboost_tune)
## # A tibble: 15 × 12
##    trees min_n tree_depth learn_rate loss_reduction sample_size .metric    
##    <int> <int>      <int>      <dbl>          <dbl>       <dbl> <chr>      
##  1   598     4          3    0.0109        1.66e-10       0.126 accuracy   
##  2   598     4          3    0.0109        1.66e-10       0.126 brier_class
##  3   598     4          3    0.0109        1.66e-10       0.126 roc_auc    
##  4   927    17         13    0.00662       7.86e- 7       0.497 accuracy   
##  5   927    17         13    0.00662       7.86e- 7       0.497 brier_class
##  6   927    17         13    0.00662       7.86e- 7       0.497 roc_auc    
##  7  1388    20          6    0.00235       3.17e- 4       0.849 accuracy   
##  8  1388    20          6    0.00235       3.17e- 4       0.849 brier_class
##  9  1388    20          6    0.00235       3.17e- 4       0.849 roc_auc    
## 10   213    28          9    0.0464        2.23e- 3       0.655 accuracy   
## 11   213    28          9    0.0464        2.23e- 3       0.655 brier_class
## 12   213    28          9    0.0464        2.23e- 3       0.655 roc_auc    
## 13  1951    35          7    0.235         2.23e- 1       0.385 accuracy   
## 14  1951    35          7    0.235         2.23e- 1       0.385 brier_class
## 15  1951    35          7    0.235         2.23e- 1       0.385 roc_auc    
## # ℹ 5 more variables: .estimator <chr>, mean <dbl>, n <int>, std_err <dbl>,
## #   .config <chr>
collect_predictions(xgboost_tune) %>%
    group_by(id) %>% 
    roc_curve(died, .pred_Died) %>%
    autoplot()

Fit the models for the last time

xgboost_last <- xgboost_workflow1 %>%
    finalize_workflow(select_best(xgboost_tune, metric = "accuracy")) %>%
    last_fit(members_split)

collect_metrics(xgboost_last)
## # A tibble: 3 × 4
##   .metric     .estimator .estimate .config             
##   <chr>       <chr>          <dbl> <chr>               
## 1 accuracy    binary        0.985  Preprocessor1_Model1
## 2 roc_auc     binary        0.776  Preprocessor1_Model1
## 3 brier_class binary        0.0145 Preprocessor1_Model1
collect_predictions(xgboost_last) %>%
    yardstick::conf_mat(died, .pred_class) %>%
    autoplot()

Varible importance

library(vip)
## 
## Attaching package: 'vip'
## The following object is masked from 'package:utils':
## 
##     vi
xgboost_last %>%
    workflows::extract_fit_engine() %>%
    vip()