Apply it to your data 8

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

## ── Attaching packages ────────────────────────────────────── tidymodels 1.1.1 ──
## ✔ broom        1.0.5     ✔ rsample      1.2.0
## ✔ dials        1.2.0     ✔ tune         1.1.2
## ✔ infer        1.0.6     ✔ workflows    1.1.3
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## ✔ parsnip      1.1.1     ✔ yardstick    1.3.0
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## • Use tidymodels_prefer() to resolve common conflicts.

library(textrecipes) # for preprocessing string
library(xgboost)

## 
## Attaching package: 'xgboost'
## 
## The following object is masked from 'package:dplyr':
## 
##     slice

library(tidytext)
library(Binarize)

## Loading required package: diptest

library(correlation)
library(correlationfunnel)

## ══ Using correlationfunnel? ════════════════════════════════════════════════════
## You might also be interested in applied data science training for business.
## </> Learn more at - www.business-science.io </>

library(doParallel)

## Loading required package: foreach
## 
## Attaching package: 'foreach'
## 
## The following objects are masked from 'package:purrr':
## 
##     accumulate, when
## 
## Loading required package: iterators
## Loading required package: parallel

library(LiblineaR)
library(ranger)
library(vip)

## 
## Attaching package: 'vip'
## 
## The following object is masked from 'package:utils':
## 
##     vi

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, -peak_id) %>%
  na.omit() %>%
  
  # 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: 57
## $ 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, …
## $ `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: 57 × 3
##    feature     bin       correlation
##    <fct>       <chr>           <dbl>
##  1 died        DIED           1     
##  2 died        no            -1     
##  3 year        -Inf_1992      0.0519
##  4 success     FALSE          0.0332
##  5 success     TRUE          -0.0332
##  6 year        2004_2012     -0.0211
##  7 year        2012_Inf      -0.0208
##  8 sex         F             -0.0168
##  9 sex         M              0.0168
## 10 citizenship USA           -0.0154
## # ℹ 47 more rows

# Step 3: Plot 
data_corr_tbl %>%
  plot_correlation_funnel()

## Warning: ggrepel: 27 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Point Chart Death vs Season

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

Model Building

Split Data

set.seed(1234)
data <- data %>% group_by(died) %>%  
  sample_n(929)

data_split <- initial_split(data, 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 [1252/140]> Fold01
##  2 <split [1252/140]> Fold02
##  3 <split [1252/140]> Fold03
##  4 <split [1252/140]> Fold04
##  5 <split [1252/140]> Fold05
##  6 <split [1252/140]> Fold06
##  7 <split [1254/138]> Fold07
##  8 <split [1254/138]> Fold08
##  9 <split [1254/138]> Fold09
## 10 <split [1254/138]> 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)
library(lattice)

xgboost_rec <- recipes::recipe(died ~., data = data_train) %>% 
    update_role(member_id, new_role = "ID") %>%
    step_other(peak_name, citizenship, expedition_role) %>%
    step_dummy(all_nominal_predictors(), one_hot = TRUE) %>%
    step_normalize(all_numeric_predictors())

xgboost_rec %>% prep() %>% juice() %>% glimpse()

## Rows: 1,392
## Columns: 1,162
## $ member_id                  <fct> KANG92101-06, EVER15154-15, DHA178101-12, G…
## $ year                       <dbl> -0.3498660, 1.1349311, -1.2536555, 1.328600…
## $ age                        <dbl> 1.19812999, 2.47073434, -0.36815229, -1.347…
## $ died                       <fct> DIED, DIED, DIED, DIED, DIED, DIED, DIED, D…
## $ expedition_id_AMAD00104    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD00317    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD01311    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD02305    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD02318    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD02404    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD03316    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD04402    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD05101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD05321    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD06104    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD06322    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD06335    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_AMAD06344    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD07309    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD07320    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD07337    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD08301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD08311    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD08315    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD08317    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_AMAD08327    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD09325    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD09346    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD10301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD10309    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD11321    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD11351    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD11358    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD13103    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD13305    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD13311    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD13317    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD13331    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD14104    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_AMAD14312    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD14322    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD14334    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD14338    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD14355    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD15322    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD15325    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD16306    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD16312    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD16321    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD16329    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD16353    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD17310    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD17340    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD18105    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD18106    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD18301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD18302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD18315    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD18330    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD18340    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD18348    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD18401    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD59101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_AMAD79302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
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## $ expedition_id_MANA72101    <dbl> -0.06577153, -0.06577153, -0.06577153, -0.0…
## $ expedition_id_MANA72102    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_MANA75101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANA79301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANA82101    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_MANA82401    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANA83101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANA83402    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANA84301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANA85301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANA85303    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANA86101    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_MANA87302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANA90302    <dbl> -0.04645725, -0.04645725, -0.04645725, -0.0…
## $ expedition_id_MANA91104    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_MANA92303    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_MANA93303    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_MANA93304    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANA95101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANA96301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANA96305    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANA96308    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANA97307    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANA98306    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANA99303    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANN79301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MANN85301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MERR09301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_MUKT15303    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NAG106301    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_NAG216301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NALA00201    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_NEMJ04301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NEMJ64301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NEPA37101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NEPA95301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NGOJ85101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NGOJ87401    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NILS15301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NUMB82101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NUPE97301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NUPT09301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NUPT15101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NUPT17101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NUPT75101    <dbl> -0.04645725, -0.04645725, -0.04645725, -0.0…
## $ expedition_id_NUPT77101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NUPT97101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NUPT97301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_NUPW97301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PISA94301    <dbl> -0.08064067, -0.08064067, -0.08064067, -0.0…
## $ expedition_id_PK2970301    <dbl> -0.04645725, -0.04645725, -0.04645725, -0.0…
## $ expedition_id_PK2978301    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_PKAR18101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO00101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO01302    <dbl> -0.04645725, -0.04645725, -0.04645725, -0.0…
## $ expedition_id_PUMO03103    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO03302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO03303    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO04303    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO05103    <dbl> -0.04645725, -0.04645725, -0.04645725, -0.0…
## $ expedition_id_PUMO05104    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_PUMO06302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO06305    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_PUMO07301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO08302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO11307    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO75101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO78101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO78301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO83101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO84401    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO88102    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO88302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO88304    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_PUMO89302    <dbl> -0.05366353, -0.05366353, -0.05366353, -0.0…
## $ expedition_id_PUMO89401    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO90310    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO91301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO91302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO92102    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_PUMO92304    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO93302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO94302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO97102    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO97301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUMO99304    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUTH04302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUTH14302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUTH15301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUTH16303    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUTH73301    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_PUTH96302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_PUTH97301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_RAKS03301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_RAMT98302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_RATH64301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_RAUN15301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_SARI02201    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_SHA288301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_SHEY95301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_SHNW80401    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TAPL63101    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_TARS84301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TAWO15301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TAWO85401    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_THUL11101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TILI03301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TILI08303    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TILI08304    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TILI86101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TILI88301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TILI92301    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_TILI99302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TILK18301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TKPO10301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TLNG55101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TUKU08302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TUKU14302    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TUKU70101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TUKU72301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TUKU83301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_TUKU85101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_URMA13401    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_YAKA08201    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_YALU14101    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_YALU73101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_YALU74101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_YALU80101    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_YALU89401    <dbl> -0.03791853, -0.03791853, -0.03791853, -0.0…
## $ expedition_id_YAUP17101    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ expedition_id_YAUP89301    <dbl> -0.02680281, -0.02680281, -0.02680281, -0.0…
## $ peak_name_Ama.Dablam       <dbl> -0.253326, -0.253326, -0.253326, -0.253326,…
## $ peak_name_Cho.Oyu          <dbl> -0.3070372, -0.3070372, -0.3070372, -0.3070…
## $ peak_name_Dhaulagiri.I     <dbl> -0.2517174, -0.2517174, 3.9698557, -0.25171…
## $ peak_name_Everest          <dbl> -0.6414584, 1.5578276, -0.6414584, -0.64145…
## $ peak_name_Manaslu          <dbl> -0.2549269, -0.2549269, -0.2549269, -0.2549…
## $ peak_name_other            <dbl> 1.1252514, -0.8880519, -0.8880519, 1.125251…
## $ season_Autumn              <dbl> -0.8764644, -0.8764644, -0.8764644, 1.14012…
## $ season_Spring              <dbl> 0.9602026, 0.9602026, 0.9602026, -1.0406987…
## $ season_Summer              <dbl> -0.07106702, -0.07106702, -0.07106702, -0.0…
## $ season_Winter              <dbl> -0.2046608, -0.2046608, -0.2046608, -0.2046…
## $ sex_F                      <dbl> 3.6525230, -0.2735867, -0.2735867, -0.27358…
## $ sex_M                      <dbl> -3.6525230, 0.2735867, 0.2735867, 0.2735867…
## $ citizenship_France         <dbl> -0.2565202, -0.2565202, -0.2565202, -0.2565…
## $ citizenship_Japan          <dbl> -0.3564185, -0.3564185, 2.8036746, -0.35641…
## $ citizenship_Nepal          <dbl> -0.4915026, 2.0331158, -0.4915026, 2.033115…
## $ citizenship_S.Korea        <dbl> -0.243553, -0.243553, -0.243553, -0.243553,…
## $ citizenship_UK             <dbl> -0.2581061, -0.2581061, -0.2581061, -0.2581…
## $ citizenship_USA            <dbl> -0.2690113, -0.2690113, -0.2690113, -0.2690…
## $ citizenship_other          <dbl> 1.1170905, -0.8945395, -0.8945395, -0.89453…
## $ expedition_role_Climber    <dbl> 0.8398662, -1.1898105, 0.8398662, -1.189810…
## $ expedition_role_H.A.Worker <dbl> -0.4284175, -0.4284175, -0.4284175, -0.4284…
## $ expedition_role_Leader     <dbl> -0.4424201, -0.4424201, -0.4424201, -0.4424…
## $ expedition_role_other      <dbl> -0.3235532, 3.0884619, -0.3235532, 3.088461…
## $ hired_FALSE.               <dbl> 0.4756775, -2.1007546, 0.4756775, -2.100754…
## $ hired_TRUE.                <dbl> -0.4756775, 2.1007546, -0.4756775, 2.100754…
## $ success_FALSE.             <dbl> 0.6705794, 0.6705794, 0.6705794, 0.6705794,…
## $ success_TRUE.              <dbl> -0.6705794, -0.6705794, -0.6705794, -0.6705…
## $ solo_FALSE.                <dbl> 0.08064067, 0.08064067, 0.08064067, 0.08064…
## $ solo_TRUE.                 <dbl> -0.08064067, -0.08064067, -0.08064067, -0.0…
## $ oxygen_used_FALSE.         <dbl> 0.5505932, 0.5505932, 0.5505932, 0.5505932,…
## $ oxygen_used_TRUE.          <dbl> -0.5505932, -0.5505932, -0.5505932, -0.5505…
## $ injured_FALSE.             <dbl> 0.1206929, 0.1206929, 0.1206929, 0.1206929,…
## $ injured_TRUE.              <dbl> -0.1206929, -0.1206929, -0.1206929, -0.1206…

Make the model specific

library(usemodels)

usemodels::use_xgboost(died ~., data = data_train)

## Adding missing grouping variables: `died`

## xgboost_recipe <- 
##   recipe(formula = died ~ ., data = data_train) %>% 
##   step_string2factor(one_of(NA_character_)) %>% 
##   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(80544)
## 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,
                          control = control_grid(save_pred = TRUE))

Evaluation

Identification of the Optimal Values for Hyperparameters

collect_predictions(xgboost_tune) %>%
    group_by(id) %>%
    roc_curve(died, .pred_DIED) %>%
    autoplot()

Finalize by fitting the model

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

## → A | warning: ! There are new levels in a factor: TILI05301, CHOY10327, CHOY91106, EVER95302,
##                  EVER89401, LSIS73301, EVER92313, DHA198102, KANG13106, EVER93307, EVER99134,
##                  NGO202101, AMAD06403, PUMO61401, ANNE88301, TILI06302, HIML16301, CHOY88302,
##                  …, HUNK12302, and MAKA97302

## 
There were issues with some computations   A: x1

There were issues with some computations   A: x1

collect_metrics(xgboost_last)

## # A tibble: 2 × 4
##   .metric  .estimator .estimate .config             
##   <chr>    <chr>          <dbl> <chr>               
## 1 accuracy binary         0.682 Preprocessor1_Model1
## 2 roc_auc  binary         0.737 Preprocessor1_Model1

collect_predictions(xgboost_last) %>%
    yardstick::conf_mat(died, .pred_class) %>%
    autoplot()

Variable Importance

library(vip)

xgboost_last %>%
  workflows::extract_fit_engine() %>%
  vip()

Conclusion

In the previous model I had an accuracy of 0.685, and a AUC of 0.736

In this model I decided to normalize the numeric data, because of this I ended up seeing there was no improvement on the AUC, but there was a very slight decrease in accuracy by 0.011%.