Code Along 12: Employee Attrition
Erin McEvoy
2024-12-03
Prompts I Used: I have a dataset called attrition_raw_tbl that looks like this.
attrition_raw_tbl %>% glimpse() Rows: 1,470 Columns: 35 $ Age
The goal is to help predict attrition for employees.
Please write R code to create a predictive model that predicts the probability of attrition. ”
Prompt 2: “Please update the code to use tidymodels instead of caret and to use the h2o model instead of glmnet.”
Prompt 3: “Error in add_model(): ! parsnip could not locate an implementation for rand_forest classification model specifications using the h2o engine. ℹ The parsnip extension package agua implements support for this specification. ℹ Please install (if needed) and load to continue.
Backtrace: 1. workflow() %>% add_recipe(attrition_recipe) %>% … 2. workflows::add_model(., rf_model)”
Prompt 4: “Error in step_dummy(): Caused by error in bake(): ! Only one factor level in col_name: Y. Backtrace: 1. generics::fit(rf_workflow, data = train_data) 2. workflows:::fit.workflow(rf_workflow, data = train_data) 3. workflows::.fit_pre(workflow, data) 5. workflows:::fit.action_recipe(action, workflow = workflow, data = data) 7. hardhat:::mold.recipe(recipe, data, blueprint = blueprint) 9. hardhat:::run_mold.default_recipe_blueprint(blueprint, data = data) 10. hardhat:::mold_recipe_default_process(…) 12. recipes:::prep.recipe(…) 17. recipes:::bake.step_dummy(x$steps[[i]], new_data = training)”
Prompt 5: “the error is in col_names”
Prompt 6: “Error in confusionMatrix(factor(pred_class), factor(test_data$Attrition)) : could not find function”confusionMatrix””
# Load required libraries
library(tidymodels) # For tidymodels## ── Attaching packages ────────────────────────────────────── tidymodels 1.2.0 ──## ✔ broom 1.0.6 ✔ recipes 1.1.0
## ✔ dials 1.2.1 ✔ rsample 1.2.1
## ✔ dplyr 1.1.4 ✔ tibble 3.2.1
## ✔ ggplot2 3.5.1 ✔ tidyr 1.3.1
## ✔ infer 1.0.7 ✔ tune 1.2.1
## ✔ modeldata 1.4.0 ✔ workflows 1.1.4
## ✔ parsnip 1.2.1 ✔ workflowsets 1.1.0
## ✔ purrr 1.0.2 ✔ yardstick 1.3.1## Warning: package 'broom' was built under R version 4.3.3## Warning: package 'modeldata' was built under R version 4.3.3## Warning: package 'recipes' was built under R version 4.3.3## ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
## ✖ purrr::discard() masks scales::discard()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ recipes::step() masks stats::step()
## • Use suppressPackageStartupMessages() to eliminate package startup messageslibrary(h2o) # For h2o models##
## ----------------------------------------------------------------------
##
## Your next step is to start H2O:
## > h2o.init()
##
## For H2O package documentation, ask for help:
## > ??h2o
##
## After starting H2O, you can use the Web UI at http://localhost:54321
## For more information visit https://docs.h2o.ai
##
## ----------------------------------------------------------------------##
## Attaching package: 'h2o'## The following objects are masked from 'package:stats':
##
## cor, sd, var## The following objects are masked from 'package:base':
##
## &&, %*%, %in%, ||, apply, as.factor, as.numeric, colnames,
## colnames<-, ifelse, is.character, is.factor, is.numeric, log,
## log10, log1p, log2, round, signif, trunclibrary(pROC) # For ROC and AUC## Type 'citation("pROC")' for a citation.##
## Attaching package: 'pROC'## The following object is masked from 'package:h2o':
##
## var## The following objects are masked from 'package:stats':
##
## cov, smooth, varlibrary(agua)## Warning: package 'agua' was built under R version 4.3.3##
## Attaching package: 'agua'## The following object is masked from 'package:workflowsets':
##
## rank_resultslibrary(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ readr 2.1.5
## ✔ lubridate 1.9.3 ✔ stringr 1.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ readr::col_factor() masks scales::col_factor()
## ✖ lubridate::day() masks h2o::day()
## ✖ purrr::discard() masks scales::discard()
## ✖ dplyr::filter() masks stats::filter()
## ✖ stringr::fixed() masks recipes::fixed()
## ✖ lubridate::hour() masks h2o::hour()
## ✖ dplyr::lag() masks stats::lag()
## ✖ lubridate::month() masks h2o::month()
## ✖ readr::spec() masks yardstick::spec()
## ✖ lubridate::week() masks h2o::week()
## ✖ lubridate::year() masks h2o::year()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsattrition_raw_tbl <- readr::read_csv("WA_Fn-UseC_-HR-Employee-Attrition.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.# If data is not sensitive:
attrition_raw_tbl %>% glimpse()## Rows: 1,470
## Columns: 35
## $ Age <dbl> 41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35, 2…
## $ Attrition <chr> "Yes", "No", "Yes", "No", "No", "No", "No", "…
## $ BusinessTravel <chr> "Travel_Rarely", "Travel_Frequently", "Travel…
## $ DailyRate <dbl> 1102, 279, 1373, 1392, 591, 1005, 1324, 1358,…
## $ Department <chr> "Sales", "Research & Development", "Research …
## $ DistanceFromHome <dbl> 1, 8, 2, 3, 2, 2, 3, 24, 23, 27, 16, 15, 26, …
## $ Education <dbl> 2, 1, 2, 4, 1, 2, 3, 1, 3, 3, 3, 2, 1, 2, 3, …
## $ EducationField <chr> "Life Sciences", "Life Sciences", "Other", "L…
## $ EmployeeCount <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ EmployeeNumber <dbl> 1, 2, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 16,…
## $ EnvironmentSatisfaction <dbl> 2, 3, 4, 4, 1, 4, 3, 4, 4, 3, 1, 4, 1, 2, 3, …
## $ Gender <chr> "Female", "Male", "Male", "Female", "Male", "…
## $ HourlyRate <dbl> 94, 61, 92, 56, 40, 79, 81, 67, 44, 94, 84, 4…
## $ JobInvolvement <dbl> 3, 2, 2, 3, 3, 3, 4, 3, 2, 3, 4, 2, 3, 3, 2, …
## $ JobLevel <dbl> 2, 2, 1, 1, 1, 1, 1, 1, 3, 2, 1, 2, 1, 1, 1, …
## $ JobRole <chr> "Sales Executive", "Research Scientist", "Lab…
## $ JobSatisfaction <dbl> 4, 2, 3, 3, 2, 4, 1, 3, 3, 3, 2, 3, 3, 4, 3, …
## $ MaritalStatus <chr> "Single", "Married", "Single", "Married", "Ma…
## $ MonthlyIncome <dbl> 5993, 5130, 2090, 2909, 3468, 3068, 2670, 269…
## $ MonthlyRate <dbl> 19479, 24907, 2396, 23159, 16632, 11864, 9964…
## $ NumCompaniesWorked <dbl> 8, 1, 6, 1, 9, 0, 4, 1, 0, 6, 0, 0, 1, 0, 5, …
## $ Over18 <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", …
## $ OverTime <chr> "Yes", "No", "Yes", "Yes", "No", "No", "Yes",…
## $ PercentSalaryHike <dbl> 11, 23, 15, 11, 12, 13, 20, 22, 21, 13, 13, 1…
## $ PerformanceRating <dbl> 3, 4, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 3, …
## $ RelationshipSatisfaction <dbl> 1, 4, 2, 3, 4, 3, 1, 2, 2, 2, 3, 4, 4, 3, 2, …
## $ StandardHours <dbl> 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 8…
## $ StockOptionLevel <dbl> 0, 1, 0, 0, 1, 0, 3, 1, 0, 2, 1, 0, 1, 1, 0, …
## $ TotalWorkingYears <dbl> 8, 10, 7, 8, 6, 8, 12, 1, 10, 17, 6, 10, 5, 3…
## $ TrainingTimesLastYear <dbl> 0, 3, 3, 3, 3, 2, 3, 2, 2, 3, 5, 3, 1, 2, 4, …
## $ WorkLifeBalance <dbl> 1, 3, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3, 2, 3, 3, …
## $ YearsAtCompany <dbl> 6, 10, 0, 8, 2, 7, 1, 1, 9, 7, 5, 9, 5, 2, 4,…
## $ YearsInCurrentRole <dbl> 4, 7, 0, 7, 2, 7, 0, 0, 7, 7, 4, 5, 2, 2, 2, …
## $ YearsSinceLastPromotion <dbl> 0, 1, 0, 3, 2, 3, 0, 0, 1, 7, 0, 0, 4, 1, 0, …
## $ YearsWithCurrManager <dbl> 5, 7, 0, 0, 2, 6, 0, 0, 8, 7, 3, 8, 3, 2, 3, …# If data is sensitive:
attrition_raw_tbl %>%
slice(0) %>%
glimpse()## Rows: 0
## Columns: 35
## $ Age <dbl>
## $ Attrition <chr>
## $ BusinessTravel <chr>
## $ DailyRate <dbl>
## $ Department <chr>
## $ DistanceFromHome <dbl>
## $ Education <dbl>
## $ EducationField <chr>
## $ EmployeeCount <dbl>
## $ EmployeeNumber <dbl>
## $ EnvironmentSatisfaction <dbl>
## $ Gender <chr>
## $ HourlyRate <dbl>
## $ JobInvolvement <dbl>
## $ JobLevel <dbl>
## $ JobRole <chr>
## $ JobSatisfaction <dbl>
## $ MaritalStatus <chr>
## $ MonthlyIncome <dbl>
## $ MonthlyRate <dbl>
## $ NumCompaniesWorked <dbl>
## $ Over18 <chr>
## $ OverTime <chr>
## $ PercentSalaryHike <dbl>
## $ PerformanceRating <dbl>
## $ RelationshipSatisfaction <dbl>
## $ StandardHours <dbl>
## $ StockOptionLevel <dbl>
## $ TotalWorkingYears <dbl>
## $ TrainingTimesLastYear <dbl>
## $ WorkLifeBalance <dbl>
## $ YearsAtCompany <dbl>
## $ YearsInCurrentRole <dbl>
## $ YearsSinceLastPromotion <dbl>
## $ YearsWithCurrManager <dbl># Initialize h2o instance
h2o.init()##
## H2O is not running yet, starting it now...
##
## Note: In case of errors look at the following log files:
## /var/folders/t0/wjq1417j12347mw6pjh4g3580000gn/T//RtmpNfI8Fg/file869932642e73/h2o_erinmcevoy_started_from_r.out
## /var/folders/t0/wjq1417j12347mw6pjh4g3580000gn/T//RtmpNfI8Fg/file86994f2599dd/h2o_erinmcevoy_started_from_r.err
##
##
## Starting H2O JVM and connecting: ... Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 3 seconds 839 milliseconds
## H2O cluster timezone: America/New_York
## H2O data parsing timezone: UTC
## H2O cluster version: 3.44.0.3
## H2O cluster version age: 11 months and 12 days
## H2O cluster name: H2O_started_from_R_erinmcevoy_hkl777
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.77 GB
## H2O cluster total cores: 8
## H2O cluster allowed cores: 8
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
## H2O Connection port: 54321
## H2O Connection proxy: NA
## H2O Internal Security: FALSE
## R Version: R version 4.3.2 (2023-10-31)## Warning in h2o.clusterInfo():
## Your H2O cluster version is (11 months and 12 days) old. There may be a newer version available.
## Please download and install the latest version from: https://h2o-release.s3.amazonaws.com/h2o/latest_stable.html# Step 1: Clean column names to ensure they're valid
names(attrition_raw_tbl) <- make.names(names(attrition_raw_tbl))
# Step 2: Convert categorical variables to factors (this is necessary for h2o models)
attrition_raw_tbl$Attrition <- factor(attrition_raw_tbl$Attrition, levels = c("No", "Yes"))
attrition_raw_tbl$BusinessTravel <- factor(attrition_raw_tbl$BusinessTravel)
attrition_raw_tbl$Department <- factor(attrition_raw_tbl$Department)
attrition_raw_tbl$EducationField <- factor(attrition_raw_tbl$EducationField)
attrition_raw_tbl$Gender <- factor(attrition_raw_tbl$Gender)
attrition_raw_tbl$JobRole <- factor(attrition_raw_tbl$JobRole)
attrition_raw_tbl$MaritalStatus <- factor(attrition_raw_tbl$MaritalStatus)
attrition_raw_tbl$OverTime <- factor(attrition_raw_tbl$OverTime)
# Step 3: Split data into training and testing sets
set.seed(123) # For reproducibility
split <- initial_split(attrition_raw_tbl, prop = 0.8)
train_data <- training(split)
test_data <- testing(split)
# Step 4: Check for missing values and handle them (optional)
train_data <- train_data %>%
mutate(across(where(is.numeric), ~ replace_na(., median(., na.rm = TRUE)))) %>%
mutate(across(where(is.character), ~ replace_na(., "Unknown")))
test_data <- test_data %>%
mutate(across(where(is.numeric), ~ replace_na(., median(., na.rm = TRUE)))) %>%
mutate(across(where(is.character), ~ replace_na(., "Unknown")))
# Step 5: Create a recipe and handle transformations
attrition_recipe <- recipe(Attrition ~ ., data = train_data) %>%
step_zv(all_predictors()) %>%
step_dummy(all_nominal(), -all_outcomes())
# Step 6: Specify the model using `parsnip` (set up h2o random forest)
rf_model <- rand_forest(mode = "classification", trees = 1000) %>%
set_engine("h2o")
# Step 7: Create a workflow
rf_workflow <- workflow() %>%
add_recipe(attrition_recipe) %>%
add_model(rf_model)
# Step 8: Train the model
rf_fit <- fit(rf_workflow, data = train_data)
# Step 9: Make predictions on the test data
predictions <- predict(rf_fit, test_data, type = "prob")
# Step 10: Evaluate the model's performance using ROC curve
# Extract the predicted probabilities for the positive class ("Yes")
pred_prob <- predictions$.pred_Yes
# ROC Curve and AUC using pROC
roc_curve <- roc(test_data$Attrition, pred_prob)## Setting levels: control = No, case = Yes## Setting direction: controls < casesplot(roc_curve, main = "ROC Curve for Attrition Prediction")
auc(roc_curve)## Area under the curve: 0.8579# Step 11: Confusion Matrix using yardstick
# Convert predicted probabilities to class labels (Yes/No) based on a threshold of 0.5
pred_class <- ifelse(pred_prob > 0.5, "Yes", "No")
test_data$pred_class <- factor(pred_class, levels = c("No", "Yes"))
# Compute confusion matrix using yardstick
conf_matrix <- conf_mat(test_data, truth = Attrition, estimate = pred_class)
print(conf_matrix)## Truth
## Prediction No Yes
## No 245 40
## Yes 1 8# Step 12: Shutdown h2o (optional)
h2o.shutdown(prompt = FALSE)