Code Along 8: IBM HR Analytics Employee Attrition & Performance
Vanessa Riise Naas
2024-04-02
Import Data
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsdata <- read_csv("../00_data/EmployeeDataSet.csv")## Rows: 1470 Columns: 35
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Attrition, BusinessTravel, Department, EducationField, Gender, Job...
## dbl (26): Age, DailyRate, DistanceFromHome, Education, EmployeeCount, Employ...
##
## 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.Goal: Predict attrition on employees who are likely to leave the company
Issues with data:
Missing values
Factors or numeric variables:
- Education, EnvironmentSatisfactiom, JobInvolvement, JobSatisfaction, PerformanceRating, RelationshipSatisfaction, WorkLifeBalance
Zero variance variables:
- Over18, EmployeeCount, StandardHours
Character variables: Convert to numbers in the recipies step
Unbalanced target variable: Attrition
ID variable: EmployeeNumber
# Cleaning Data
# Adressing all numeric variables
factors_vec <- data %>% select(Education, EnvironmentSatisfaction, JobInvolvement, JobSatisfaction, PerformanceRating, RelationshipSatisfaction, WorkLifeBalance, JobLevel, StockOptionLevel) %>% names()
# Adressing factors imported to R as numeric variable
data_clean <- data %>%
# Adressing one variable: mutate(Education = Education %>% as.factor()) %>%
mutate(across(all_of(factors_vec), as.factor)) %>%
# Drop zero-variance variables
select(-c(Over18,EmployeeCount,StandardHours)) %>%
# Recode Attrition
mutate(Attrition = if_else(Attrition == "Yes", "Left", Attrition))Explore Data
# Adressing unnbalanced target variable
data_clean %>% count(Attrition)## # A tibble: 2 × 2
## Attrition n
## <chr> <int>
## 1 Left 237
## 2 No 1233data_clean %>%
ggplot(aes(Attrition)) +
geom_bar()
Attrition vs. MonthlyIncome
# Relationship between pay and attrition
data_clean %>%
ggplot(aes(Attrition, MonthlyIncome)) +
geom_boxplot()
Relationship in all variables with Correlation plot
library(correlationfunnel)## ══ Using correlationfunnel? ════════════════════════════════════════════════════
## You might also be interested in applied data science training for business.
## </> Learn more at - www.business-science.io </> # Step 1: Binarize
data_binarized <- data_clean %>%
select(-EmployeeNumber) %>% # ID variable
binarize()
data_binarized %>% glimpse()## Rows: 1,470
## Columns: 120
## $ `Age__-Inf_30` <dbl> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, …
## $ Age__30_36 <dbl> 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, …
## $ Age__36_43 <dbl> 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ Age__43_Inf <dbl> 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ Attrition__Left <dbl> 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ Attrition__No <dbl> 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ `BusinessTravel__Non-Travel` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ BusinessTravel__Travel_Frequently <dbl> 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, …
## $ BusinessTravel__Travel_Rarely <dbl> 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, …
## $ `DailyRate__-Inf_465` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ DailyRate__465_802 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, …
## $ DailyRate__802_1157 <dbl> 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, …
## $ DailyRate__1157_Inf <dbl> 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, …
## $ Department__Human_Resources <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `Department__Research_&_Development` <dbl> 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ Department__Sales <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `DistanceFromHome__-Inf_2` <dbl> 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, …
## $ DistanceFromHome__2_7 <dbl> 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, …
## $ DistanceFromHome__7_14 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ DistanceFromHome__14_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, …
## $ Education__1 <dbl> 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, …
## $ Education__2 <dbl> 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, …
## $ Education__3 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, …
## $ Education__4 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
## $ Education__5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ EducationField__Human_Resources <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ EducationField__Life_Sciences <dbl> 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, …
## $ EducationField__Marketing <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ EducationField__Medical <dbl> 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, …
## $ EducationField__Other <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ EducationField__Technical_Degree <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ EnvironmentSatisfaction__1 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, …
## $ EnvironmentSatisfaction__2 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ EnvironmentSatisfaction__3 <dbl> 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, …
## $ EnvironmentSatisfaction__4 <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, …
## $ Gender__Female <dbl> 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, …
## $ Gender__Male <dbl> 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, …
## $ `HourlyRate__-Inf_48` <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, …
## $ HourlyRate__48_66 <dbl> 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
## $ HourlyRate__66_83.75 <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, …
## $ HourlyRate__83.75_Inf <dbl> 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, …
## $ JobInvolvement__1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JobInvolvement__2 <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ JobInvolvement__3 <dbl> 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, …
## $ JobInvolvement__4 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, …
## $ JobLevel__1 <dbl> 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, …
## $ JobLevel__2 <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
## $ JobLevel__3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ JobLevel__4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JobLevel__5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JobRole__Healthcare_Representative <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
## $ JobRole__Human_Resources <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JobRole__Laboratory_Technician <dbl> 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, …
## $ JobRole__Manager <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JobRole__Manufacturing_Director <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ JobRole__Research_Director <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JobRole__Research_Scientist <dbl> 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
## $ JobRole__Sales_Executive <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JobRole__Sales_Representative <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JobSatisfaction__1 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ JobSatisfaction__2 <dbl> 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, …
## $ JobSatisfaction__3 <dbl> 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, …
## $ JobSatisfaction__4 <dbl> 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
## $ MaritalStatus__Divorced <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, …
## $ MaritalStatus__Married <dbl> 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, …
## $ MaritalStatus__Single <dbl> 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, …
## $ `MonthlyIncome__-Inf_2911` <dbl> 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, …
## $ MonthlyIncome__2911_4919 <dbl> 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, …
## $ MonthlyIncome__4919_8379 <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
## $ MonthlyIncome__8379_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ `MonthlyRate__-Inf_8047` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ MonthlyRate__8047_14235.5 <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, …
## $ MonthlyRate__14235.5_20461.5 <dbl> 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, …
## $ MonthlyRate__20461.5_Inf <dbl> 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
## $ `NumCompaniesWorked__-Inf_1` <dbl> 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, …
## $ NumCompaniesWorked__1_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ NumCompaniesWorked__2_4 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ NumCompaniesWorked__4_Inf <dbl> 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, …
## $ OverTime__No <dbl> 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, …
## $ OverTime__Yes <dbl> 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, …
## $ `PercentSalaryHike__-Inf_12` <dbl> 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, …
## $ PercentSalaryHike__12_14 <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, …
## $ PercentSalaryHike__14_18 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ PercentSalaryHike__18_Inf <dbl> 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, …
## $ PerformanceRating__3 <dbl> 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, …
## $ PerformanceRating__4 <dbl> 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, …
## $ RelationshipSatisfaction__1 <dbl> 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ RelationshipSatisfaction__2 <dbl> 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, …
## $ RelationshipSatisfaction__3 <dbl> 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, …
## $ RelationshipSatisfaction__4 <dbl> 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, …
## $ StockOptionLevel__0 <dbl> 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, …
## $ StockOptionLevel__1 <dbl> 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, …
## $ StockOptionLevel__2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
## $ StockOptionLevel__3 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ `TotalWorkingYears__-Inf_6` <dbl> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, …
## $ TotalWorkingYears__6_10 <dbl> 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, …
## $ TotalWorkingYears__10_15 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ TotalWorkingYears__15_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
## $ `TrainingTimesLastYear__-Inf_2` <dbl> 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, …
## $ TrainingTimesLastYear__2_3 <dbl> 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, …
## $ TrainingTimesLastYear__3_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
## $ WorkLifeBalance__1 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ WorkLifeBalance__2 <dbl> 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, …
## $ WorkLifeBalance__3 <dbl> 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, …
## $ WorkLifeBalance__4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `YearsAtCompany__-Inf_3` <dbl> 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, …
## $ YearsAtCompany__3_5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
## $ YearsAtCompany__5_9 <dbl> 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, …
## $ YearsAtCompany__9_Inf <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `YearsInCurrentRole__-Inf_2` <dbl> 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, …
## $ YearsInCurrentRole__2_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ YearsInCurrentRole__3_7 <dbl> 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, …
## $ YearsInCurrentRole__7_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `YearsSinceLastPromotion__-Inf_1` <dbl> 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, …
## $ YearsSinceLastPromotion__1_3 <dbl> 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, …
## $ YearsSinceLastPromotion__3_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
## $ `YearsWithCurrManager__-Inf_2` <dbl> 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, …
## $ YearsWithCurrManager__2_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
## $ YearsWithCurrManager__3_7 <dbl> 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, …
## $ YearsWithCurrManager__7_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …# Step 2: Correlation
data_correlation <- data_binarized %>%
correlate(Attrition__Left) # Target variable
data_correlation## # A tibble: 120 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 Attrition Left 1
## 2 Attrition No -1
## 3 OverTime Yes 0.246
## 4 OverTime No -0.246
## 5 JobLevel 1 0.213
## 6 MonthlyIncome -Inf_2911 0.207
## 7 StockOptionLevel 0 0.195
## 8 YearsAtCompany -Inf_3 0.183
## 9 MaritalStatus Single 0.175
## 10 TotalWorkingYears -Inf_6 0.169
## # ℹ 110 more rows# Step 3: Plot
data_correlation %>%
correlationfunnel::plot_correlation_funnel()## Warning: ggrepel: 72 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Model Building
Split Data
library(tidymodels)## ── 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
## ✔ modeldata 1.3.0 ✔ workflowsets 1.0.1
## ✔ parsnip 1.1.1 ✔ yardstick 1.3.0
## ✔ recipes 1.0.8## ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
## ✖ scales::discard() masks purrr::discard()
## ✖ dplyr::filter() masks stats::filter()
## ✖ recipes::fixed() masks stringr::fixed()
## ✖ dplyr::lag() masks stats::lag()
## ✖ yardstick::spec() masks readr::spec()
## ✖ recipes::step() masks stats::step()
## • Learn how to get started at https://www.tidymodels.org/start/set.seed(1234)
data_clean <- data_clean %>% sample_n(500)
data_split <- initial_split(data_clean, strata = Attrition)
data_training <- training(data_split)
data_test <- testing(data_split)
# Cross Validation
data_cv <- rsample::vfold_cv(data_training, strata = Attrition)
data_cv## # 10-fold cross-validation using stratification
## # A tibble: 10 × 2
## splits id
## <list> <chr>
## 1 <split [336/38]> Fold01
## 2 <split [336/38]> Fold02
## 3 <split [336/38]> Fold03
## 4 <split [336/38]> Fold04
## 5 <split [336/38]> Fold05
## 6 <split [337/37]> Fold06
## 7 <split [337/37]> Fold07
## 8 <split [337/37]> Fold08
## 9 <split [337/37]> Fold09
## 10 <split [338/36]> Fold10Preprocess Data
# Solving the unbalanced target variable
library(themis)
xgboost_rec <- recipes::recipe(Attrition ~ ., data = data_training) %>%
update_role(EmployeeNumber, new_role = "ID") %>%
step_dummy(all_nominal_predictors()) %>%
# Include if model gets improved by this:
# step_YeoJohnson(DistanceFromHome, MonthlyIncome, NumCompaniesWorked, PercentSalaryHike, TotalWorkingYears, starts_with("Years")) %>%
step_normalize(all_numeric_predictors()) %>%
# step_pca(all_nominal_predictors(), threshold = .99) %>%
step_smote(Attrition)
xgboost_rec %>% prep() %>% juice() %>% glimpse()## Rows: 638
## Columns: 64
## $ Age <dbl> -0.224477519, -1.210889173, -0.55328…
## $ DailyRate <dbl> -0.4781680, 0.8517798, 1.1385815, -0…
## $ DistanceFromHome <dbl> 0.69063582, -0.08658925, -0.86381432…
## $ EmployeeNumber <dbl> 1010, 796, 1692, 161, 840, 787, 1372…
## $ HourlyRate <dbl> -1.30338779, -1.35243367, 1.44318167…
## $ MonthlyIncome <dbl> -0.57365753, -0.24406941, -1.0629387…
## $ MonthlyRate <dbl> -0.5691381, -1.5384076, 1.4742092, -…
## $ NumCompaniesWorked <dbl> -0.7012057, 1.3444335, -0.7012057, 2…
## $ PercentSalaryHike <dbl> 2.45237158, 0.50446236, -0.88690137,…
## $ TotalWorkingYears <dbl> -0.8187896, -0.6863383, -1.3485946, …
## $ TrainingTimesLastYear <dbl> -0.6390327, -0.6390327, -0.6390327, …
## $ YearsAtCompany <dbl> -0.5334685, -0.5334685, -1.0683673, …
## $ YearsInCurrentRole <dbl> -0.63688360, -0.34886127, -1.2129282…
## $ YearsSinceLastPromotion <dbl> -0.69141891, -0.36200404, -0.6914189…
## $ YearsWithCurrManager <dbl> -0.61497409, -0.61497409, -1.1814773…
## $ Attrition <fct> Left, Left, Left, Left, Left, Left, …
## $ BusinessTravel_Travel_Frequently <dbl> -0.5126489, -0.5126489, -0.5126489, …
## $ BusinessTravel_Travel_Rarely <dbl> 0.6696344, 0.6696344, 0.6696344, 0.6…
## $ Department_Research...Development <dbl> 0.7118329, -1.4010679, 0.7118329, 0.…
## $ Department_Sales <dbl> -0.6487871, 1.5372164, -0.6487871, -…
## $ Education_X2 <dbl> -0.4876199, -0.4876199, -0.4876199, …
## $ Education_X3 <dbl> -0.7462174, 1.3365088, -0.7462174, -…
## $ Education_X4 <dbl> 1.5571423, -0.6404849, 1.5571423, 1.…
## $ Education_X5 <dbl> -0.1738448, -0.1738448, -0.1738448, …
## $ EducationField_Life.Sciences <dbl> -0.8172223, -0.8172223, 1.2203854, 1…
## $ EducationField_Marketing <dbl> -0.355201, -0.355201, -0.355201, -0.…
## $ EducationField_Medical <dbl> -0.7033305, -0.7033305, -0.7033305, …
## $ EducationField_Other <dbl> 3.9946488, -0.2496656, -0.2496656, -…
## $ EducationField_Technical.Degree <dbl> -0.2949171, 3.3817166, -0.2949171, -…
## $ EnvironmentSatisfaction_X2 <dbl> -0.4834221, -0.4834221, -0.4834221, …
## $ EnvironmentSatisfaction_X3 <dbl> 1.5372164, -0.6487871, -0.6487871, -…
## $ EnvironmentSatisfaction_X4 <dbl> -0.6738217, 1.4801040, 1.4801040, -0…
## $ Gender_Male <dbl> 0.8172223, 0.8172223, 0.8172223, -1.…
## $ JobInvolvement_X2 <dbl> 1.8555727, 1.8555727, -0.5374762, -0…
## $ JobInvolvement_X3 <dbl> -1.2985095, -1.2985095, 0.7680546, 0…
## $ JobInvolvement_X4 <dbl> -0.3358465, -0.3358465, -0.3358465, …
## $ JobLevel_X2 <dbl> -0.7549177, 1.3211059, -0.7549177, -…
## $ JobLevel_X3 <dbl> -0.4322046, -0.4322046, -0.4322046, …
## $ JobLevel_X4 <dbl> -0.2496656, -0.2496656, -0.2496656, …
## $ JobLevel_X5 <dbl> -0.2435796, -0.2435796, -0.2435796, …
## $ JobRole_Human.Resources <dbl> -0.1969388, -0.1969388, -0.1969388, …
## $ JobRole_Laboratory.Technician <dbl> 2.1774148, -0.4580322, 2.1774148, -0…
## $ JobRole_Manager <dbl> -0.2729705, -0.2729705, -0.2729705, …
## $ JobRole_Manufacturing.Director <dbl> -0.3407442, -0.3407442, -0.3407442, …
## $ JobRole_Research.Director <dbl> -0.2496656, -0.2496656, -0.2496656, …
## $ JobRole_Research.Scientist <dbl> -0.4876199, -0.4876199, -0.4876199, …
## $ JobRole_Sales.Executive <dbl> -0.5209427, 1.9144644, -0.5209427, -…
## $ JobRole_Sales.Representative <dbl> -0.2310368, -0.2310368, -0.2310368, …
## $ JobSatisfaction_X2 <dbl> 2.0278715, -0.4918094, 2.0278715, 2.…
## $ JobSatisfaction_X3 <dbl> -0.6822162, -0.6822162, -0.6822162, …
## $ JobSatisfaction_X4 <dbl> -0.6822162, -0.6822162, -0.6822162, …
## $ MaritalStatus_Married <dbl> -0.9365072, -0.9365072, -0.9365072, …
## $ MaritalStatus_Single <dbl> -0.6280644, 1.5879363, 1.5879363, -0…
## $ OverTime_Yes <dbl> 1.5471208, -0.6446337, -0.6446337, 1…
## $ PerformanceRating_X4 <dbl> 2.4050971, -0.4146719, -0.4146719, -…
## $ RelationshipSatisfaction_X2 <dbl> -0.4959911, -0.4959911, -0.4959911, …
## $ RelationshipSatisfaction_X3 <dbl> -0.7075774, 1.4094942, -0.7075774, -…
## $ RelationshipSatisfaction_X4 <dbl> 1.6418718, -0.6074324, -0.6074324, -…
## $ StockOptionLevel_X1 <dbl> 1.1612862, -0.8588117, -0.8588117, -…
## $ StockOptionLevel_X2 <dbl> -0.355201, -0.355201, -0.355201, -0.…
## $ StockOptionLevel_X3 <dbl> -0.2785709, -0.2785709, -0.2785709, …
## $ WorkLifeBalance_X2 <dbl> -0.5868564, 1.6994383, -0.5868564, -…
## $ WorkLifeBalance_X3 <dbl> -1.1612862, -1.1612862, 0.8588117, 0…
## $ WorkLifeBalance_X4 <dbl> -0.3646599, -0.3646599, -0.3646599, …Specify Model
xgboost_spec <-
boost_tree(trees = tune(), tree_depth = tune()) %>%
set_mode("classification") %>%
set_engine("xgboost")
xgboost_workflow <-
workflow() %>%
add_recipe(xgboost_rec) %>%
add_model(xgboost_spec) Tune Hyperparameters
tree_grid <- grid_regular(trees(),
tree_depth(),
levels = 5)
doParallel::registerDoParallel()
set.seed(45034)
xgboost_tune <-
tune_grid(xgboost_workflow,
resamples = data_cv,
grid = 5,
control = control_grid(save_pred = TRUE))Model Evaluation
Identifying optimal values for hyperparameters
collect_metrics(xgboost_tune)## # A tibble: 10 × 8
## trees tree_depth .metric .estimator mean n std_err .config
## <int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 1311 2 accuracy binary 0.859 10 0.0129 Preprocessor1_Model1
## 2 1311 2 roc_auc binary 0.696 10 0.0269 Preprocessor1_Model1
## 3 641 5 accuracy binary 0.848 10 0.0113 Preprocessor1_Model2
## 4 641 5 roc_auc binary 0.704 10 0.0279 Preprocessor1_Model2
## 5 805 8 accuracy binary 0.856 10 0.0141 Preprocessor1_Model3
## 6 805 8 roc_auc binary 0.689 10 0.0318 Preprocessor1_Model3
## 7 1883 10 accuracy binary 0.856 10 0.0129 Preprocessor1_Model4
## 8 1883 10 roc_auc binary 0.697 10 0.0264 Preprocessor1_Model4
## 9 215 13 accuracy binary 0.848 10 0.0123 Preprocessor1_Model5
## 10 215 13 roc_auc binary 0.707 10 0.0304 Preprocessor1_Model5collect_predictions(xgboost_tune) %>%
group_by(id) %>%
roc_curve(Attrition, .pred_Left) %>%
autoplot()
Fit the model for the last time
xgboost_last <- xgboost_workflow %>%
finalize_workflow(select_best(xgboost_tune, metric = "accuracy")) %>%
last_fit(data_split)
collect_metrics(xgboost_last)## # A tibble: 2 × 4
## .metric .estimator .estimate .config
## <chr> <chr> <dbl> <chr>
## 1 accuracy binary 0.825 Preprocessor1_Model1
## 2 roc_auc binary 0.736 Preprocessor1_Model1collect_predictions(xgboost_last) %>%
yardstick::conf_mat(Attrition, .pred_class) %>%
autoplot()
library(vip)##
## Attaching package: 'vip'## The following object is masked from 'package:utils':
##
## vixgboost_last %>%
workflows::extract_fit_engine() %>%
vip()
Conlusion
The previous model had accuracy of 0.731 and AUC of 0.633
Feature transformation: Normalized numeric data, which resulted in a slight improvement in accuracy of 0.817 and AUC of 0.686
Feature transformation: YeoJohnson transformation made no improvement
Feature selection: PCA did not make an improvement