Goal to predict attrition, employees who are likely to leave the company

library(tidyverse)
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library(parameters)
library(tidymodels) # for model building
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## • Dig deeper into tidy modeling with R at https://www.tmwr.org
library(textrecipes) # for preprocessing string
library(xgboost)
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library(tidytext)
library(Binarize)
## Loading required package: diptest
library(correlation)
library(correlationfunnel)
## ══ correlationfunnel Tip #3 ════════════════════════════════════════════════════
## Using `binarize()` with data containing many columns or many rows can increase dimensionality substantially.
## Try subsetting your data column-wise or row-wise to avoid creating too many columns.
## You can always make a big problem smaller by sampling. :)
library(doParallel)
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library(LiblineaR)
library(ranger)
library(vip)
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library(themis)

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

data <- members %>%

 # Treat missing values 
  select(-death_cause, -injury_type, -highpoint_metres, -death_height_metres, -injury_height_metres) %>%
  na.omit() %>%
  
 # Drop observations that include missing variables
  drop_na() %>%
  
  # Mutate Important Variables
  mutate(died = case_when(died == "TRUE" ~ "DIED", died == "FALSE" ~ "no")) %>%
  
  mutate(across(where(is.logical), as.factor))
data %>% count(died)
## # A tibble: 2 × 2
##   died      n
##   <chr> <int>
## 1 DIED    929
## 2 no    72056
members %>%
  ggplot(aes(died)) +
  geom_bar()

Correlation Plot

# Step 1: Prepare data 
data_binarized_tbl <- data %>%
  select(-peak_name) %>%
  binarize()

data_binarized_tbl %>% glimpse()
## Rows: 72,985
## Columns: 71
## $ expedition_id__EVER88101       <dbl> 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, …
## $ `member_id__KANG10101-01`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `member_id__-OTHER`            <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ peak_id__AMAD                  <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ peak_id__ANN1                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__ANN4                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__BARU                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__CHOY                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__DHA1                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__EVER                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__HIML                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__KANG                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__LHOT                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__MAKA                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__MANA                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__PUMO                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `peak_id__-OTHER`              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `year__-Inf_1992`              <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ year__1992_2004                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ year__2004_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, 1, 1, 1, 0, 0, 0, 0, 0, …
## $ season__Spring                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 1, 0, 1, 0, 0, 1, 0, 1, …
## $ age__29_36                     <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ age__36_44                     <dbl> 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, …
## $ age__44_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, 1, 1, 1, 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__Netherlands       <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, 0, 0, 1, 0, 1, 1, 1, …
## $ citizenship__W_Germany         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, …
## $ `citizenship__-OTHER`          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ expedition_role__Climber       <dbl> 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, …
## $ expedition_role__Deputy_Leader <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ expedition_role__Exp_Doctor    <dbl> 0, 0, 0, 1, 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> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `expedition_role__-OTHER`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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, …
## $ success__FALSE                 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, …
## $ success__TRUE                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, …
## $ 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__no                       <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: Correlate 
data_corr_tbl <- data_binarized_tbl %>%
  correlate(died__DIED)
## Warning: correlate(): [Data Imbalance Detected] Consider sampling to balance the classes more than 5%
##   Column with imbalance: died__DIED
data_corr_tbl
## # A tibble: 71 × 3
##    feature bin       correlation
##    <fct>   <chr>           <dbl>
##  1 died    DIED           1     
##  2 died    no            -1     
##  3 year    -Inf_1992      0.0519
##  4 peak_id ANN1           0.0336
##  5 success FALSE          0.0332
##  6 success TRUE          -0.0332
##  7 peak_id DHA1           0.0290
##  8 peak_id AMAD          -0.0281
##  9 peak_id CHOY          -0.0241
## 10 year    2004_2012     -0.0211
## # ℹ 61 more rows
# Step 3: Plot 
data_corr_tbl %>%
  plot_correlation_funnel()
## Warning: ggrepel: 41 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Bar Chart Death vs Season

data %>%
  ggplot(aes(x = season, y = died)) +
  geom_point()

Model Building

Split Data

set.seed(0000)
data_clean <- data %>% sample_n(100)

data_split <- initial_split(data_clean, strata = died)
data_train <- training(data_split)
data_test <- testing(data_split)

data_cv <- rsample::vfold_cv(data_train, strata = died)
data_cv
## #  10-fold cross-validation using stratification 
## # A tibble: 10 × 2
##    splits         id    
##    <list>         <chr> 
##  1 <split [67/8]> Fold01
##  2 <split [67/8]> Fold02
##  3 <split [67/8]> Fold03
##  4 <split [67/8]> Fold04
##  5 <split [67/8]> Fold05
##  6 <split [68/7]> Fold06
##  7 <split [68/7]> Fold07
##  8 <split [68/7]> Fold08
##  9 <split [68/7]> Fold09
## 10 <split [68/7]> Fold10

Preprocessing The Data

library(caret)
## Loading required package: lattice
## 
## Attaching package: 'caret'
## The following objects are masked from 'package:yardstick':
## 
##     precision, recall, sensitivity, specificity
## The following object is masked from 'package:parameters':
## 
##     compare_models
## The following object is masked from 'package:purrr':
## 
##     lift
library(xgboost)
library(themis)
xgboost_rec <- recipes::recipe(died ~., data = data_train) %>% 
    update_role(peak_id, new_role = "ID") %>% 
    step_dummy(all_nominal_predictors())

xgboost_rec %>% prep() %>% juice() %>% glimpse()
## Rows: 75
## Columns: 209
## $ peak_id                            <fct> GAN4, CHOY, EVER, EVER, AMAD, BARU,…
## $ year                               <dbl> 1989, 2007, 2006, 2011, 1994, 2011,…
## $ age                                <dbl> 25, 23, 24, 49, 34, 33, 43, 41, 25,…
## $ died                               <fct> no, no, no, no, no, no, no, no, no,…
## $ expedition_id_AMAD14329            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,…
## $ expedition_id_AMAD17328            <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
## $ expedition_id_AMAD18320            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_AMAD94308            <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_AMAD99311            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_ANN286101            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_BARU08104            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_BARU11303            <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_BARU15305            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_BARU16301            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_BARU80101            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_BARU99306            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_CHOY01109            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_CHOY01326            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_CHOY02112            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_CHOY05338            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_CHOY07309            <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_CHOY09352            <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,…
## $ expedition_id_CHOY09361            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_CHOY13110            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_CHOY13324            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_CHOY16301            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_CHOY87104            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_CHOY96101            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_CHOY96109            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,…
## $ expedition_id_DHA190304            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER03144            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER03159            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER04110            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER06111            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER06193            <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER07126            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER07138            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER08139            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER09168            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER11103            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER11142            <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER14104            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER15117            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER18116            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER19125            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER19138            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER19169            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER88101            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER94107            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_EVER94201            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_GAN489301            <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_HIML01101            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_LANG89302            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_LHOT18107            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_LHOT19106            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_LIK214301            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_LSHR01101            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
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## $ expedition_id_NUPK05301            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_NUPT96301            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_PUMO08304            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
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## $ expedition_id_PUTH19101            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_id_THAM80101            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
## $ expedition_id_URMA10101            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
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## $ member_id_LANG89302.08             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
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## $ member_id_LIK214301.01             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ member_id_LSHR01101.02             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
## $ member_id_MAKA18105.01             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ member_id_MAKA54101.01             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
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## $ member_id_MUKT15302.13             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ member_id_NORB03201.01             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
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## $ member_id_NUPT96301.08             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ member_id_PUMO08304.02             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ member_id_PUMO94301.05             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ member_id_PUMO99304.10             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ member_id_PUTH19101.03             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ member_id_THAM80101.01             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
## $ member_id_URMA10101.31             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ peak_name_Annapurna.II             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ peak_name_Baruntse                 <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
## $ peak_name_Cho.Oyu                  <dbl> 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,…
## $ peak_name_Dhaulagiri.I             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ peak_name_Everest                  <dbl> 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ peak_name_Ganesh.IV                <dbl> 1, 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,…
## $ peak_name_Langtang.Lirung          <dbl> 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,…
## $ peak_name_Lhotse.Shar              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
## $ peak_name_Linkhu.Chuli.Nup         <dbl> 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,…
## $ peak_name_Manaslu                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ peak_name_Mukut.Himal              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ peak_name_Norbu.Kang               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ peak_name_Nupche.Kang              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ peak_name_Nuptse                   <dbl> 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,…
## $ peak_name_Putha.Hiunchuli          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ peak_name_Thamserku                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
## $ peak_name_Urkema                   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ season_Spring                      <dbl> 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1,…
## $ season_Summer                      <dbl> 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,…
## $ citizenship_Austria                <dbl> 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,…
## $ citizenship_Czechoslovakia         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ citizenship_Denmark                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,…
## $ citizenship_France                 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,…
## $ citizenship_Germany                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ citizenship_Greece                 <dbl> 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,…
## $ citizenship_Italy                  <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
## $ citizenship_Japan                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
## $ citizenship_Jordan                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ citizenship_Nepal                  <dbl> 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ citizenship_Netherlands            <dbl> 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,…
## $ citizenship_Norway                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ citizenship_Poland                 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ citizenship_Qatar                  <dbl> 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,…
## $ citizenship_S.Korea                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
## $ citizenship_Spain                  <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ citizenship_Sweden                 <dbl> 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,…
## $ citizenship_UK                     <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
## $ citizenship_USA                    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,…
## $ citizenship_Yugoslavia             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_role_Deputy.Leader      <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_role_H.A.Assistant      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_role_H.A.Worker         <dbl> 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ expedition_role_Leader             <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,…
## $ expedition_role_Support.Member..S. <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ hired_TRUE.                        <dbl> 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ success_TRUE.                      <dbl> 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0,…
## $ solo_TRUE.                         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ oxygen_used_TRUE.                  <dbl> 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,…
## $ injured_TRUE.                      <dbl> 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,…

Make the model specific

library(usemodels)

usemodels::use_xgboost(died ~., data = data_train)
## xgboost_recipe <- 
##   recipe(formula = died ~ ., data = data_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(34935)
## xgboost_tune <-
##   tune_grid(xgboost_workflow, resamples = stop("add your rsample object"), grid = stop("add number of candidate points"))
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_rec) %>% 
  add_model(xgboost_spec)

Tune Hyperparameters

doParallel::registerDoParallel()

set.seed(87834)
xgboost_tune <- tune_grid(xgboost_workflow,
                          resamples = data_cv,
                          grid      = 5)
## Warning: All models failed. Run `show_notes(.Last.tune.result)` for more
## information.