Apply it to Your Data 6: CEO Departures
Job Boonstoppel
2024-10-21
The dataset documents the reasons for CEO departure in S&P 1500 firms from 2000 through 2018. Goal is to predict CEO departure (ceo_dismissal) by using the departures dataset.
Import Data
library(tidyverse)## Warning: package 'ggplot2' was built under R version 4.3.1## Warning: package 'tidyr' was built under R version 4.3.1## Warning: package 'dplyr' was built under R version 4.3.1## Warning: package 'stringr' was built under R version 4.3.1## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ purrr 1.0.2
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## • Use tidymodels_prefer() to resolve common conflicts.library(textrecipes)## Warning: package 'textrecipes' was built under R version 4.3.1library(tidytext)## Warning: package 'tidytext' was built under R version 4.3.1library(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(usemodels)
data <- read.csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-04-27/departures.csv")Explore Data
skimr::skim(data)| Name | data |
| Number of rows | 9423 |
| Number of columns | 19 |
| _______________________ | |
| Column type frequency: | |
| character | 9 |
| numeric | 10 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| coname | 0 | 1.00 | 2 | 30 | 0 | 3860 | 0 |
| exec_fullname | 0 | 1.00 | 5 | 790 | 0 | 8701 | 0 |
| interim_coceo | 9105 | 0.03 | 6 | 7 | 0 | 6 | 0 |
| leftofc | 1802 | 0.81 | 20 | 20 | 0 | 3627 | 0 |
| still_there | 7311 | 0.22 | 3 | 10 | 0 | 77 | 0 |
| notes | 1644 | 0.83 | 5 | 3117 | 0 | 7755 | 0 |
| sources | 1475 | 0.84 | 18 | 1843 | 0 | 7915 | 0 |
| eight_ks | 4499 | 0.52 | 69 | 3884 | 0 | 4914 | 0 |
| X_merge | 0 | 1.00 | 11 | 11 | 0 | 1 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| dismissal_dataset_id | 0 | 1.00 | 5684.10 | 25005.46 | 1 | 2305.5 | 4593 | 6812.5 | 559044 | ▇▁▁▁▁ |
| gvkey | 0 | 1.00 | 40132.48 | 53921.34 | 1004 | 7337.0 | 14385 | 60900.5 | 328795 | ▇▁▁▁▁ |
| fyear | 0 | 1.00 | 2007.74 | 8.19 | 1987 | 2000.0 | 2008 | 2016.0 | 2020 | ▁▆▅▅▇ |
| co_per_rol | 0 | 1.00 | 25580.22 | 18202.38 | -1 | 8555.5 | 22980 | 39275.5 | 64602 | ▇▆▅▃▃ |
| departure_code | 1667 | 0.82 | 5.20 | 1.53 | 1 | 5.0 | 5 | 7.0 | 9 | ▁▃▇▅▁ |
| ceo_dismissal | 1813 | 0.81 | 0.20 | 0.40 | 0 | 0.0 | 0 | 0.0 | 1 | ▇▁▁▁▂ |
| tenure_no_ceodb | 0 | 1.00 | 1.03 | 0.17 | 0 | 1.0 | 1 | 1.0 | 3 | ▁▇▁▁▁ |
| max_tenure_ceodb | 0 | 1.00 | 1.05 | 0.24 | 1 | 1.0 | 1 | 1.0 | 4 | ▇▁▁▁▁ |
| fyear_gone | 1802 | 0.81 | 2006.64 | 13.63 | 1980 | 2000.0 | 2007 | 2013.0 | 2997 | ▇▁▁▁▁ |
| cik | 245 | 0.97 | 741469.17 | 486551.43 | 1750 | 106413.0 | 857323 | 1050375.8 | 1808065 | ▆▁▇▂▁ |
Issues with the data:
Missing Values:
* interim_coceo (91% missing)
* eight_ks (52% missing)Factors or Numeric Variables:
* departure_code (categorical but currently numeric)
* interim_coceo (needs to be a factor)
* leftofc (needs to be a factor)
* still_there (needs to be a factor)Zero Variance Variables:
* X-mergeCharacter Variables:
* coname
* exec_fullname
* sourcesUnbalanced Target Variable:
* ceo_dismissalID Variable:
* dismissal_dataset_id
* gvkey
* cikClean Data
# Clean the data and ensure ceo_dismissal is a factor
data_clean <- data %>%
mutate(leftofc = as.Date(leftofc, format = "%Y-%m-%dT"))
# Identify factor variables
factors_vec <- c("departure_code", "leftofc")
data_clean <- data_clean %>%
# Convert ceo_dismissal and factors to proper types
mutate(ceo_dismissal = as.factor(ceo_dismissal),
across(all_of(factors_vec), as.factor)) %>%
# Remove rows with missing values in key columns
drop_na(ceo_dismissal, tenure_no_ceodb, fyear_gone, departure_code, leftofc) %>%
# Drop zero-variance and irrelevant columns
select(-c(X_merge, interim_coceo, eight_ks, dismissal_dataset_id, gvkey, cik, coname, exec_fullname, sources, still_there, leftofc))
skimr::skim(data_clean)| Name | data_clean |
| Number of rows | 7478 |
| Number of columns | 8 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 2 |
| numeric | 5 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| notes | 17 | 1 | 5 | 3117 | 0 | 7451 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| departure_code | 0 | 1 | FALSE | 7 | 5: 3577, 7: 2030, 3: 1316, 4: 194 |
| ceo_dismissal | 0 | 1 | FALSE | 2 | 0: 5994, 1: 1484 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| fyear | 0 | 1 | 2005.61 | 7.45 | 1987 | 1999.0 | 2006.0 | 2012.00 | 2020 | ▁▇▆▇▆ |
| co_per_rol | 0 | 1 | 21446.28 | 16354.08 | -1 | 6970.5 | 18267.5 | 33422.75 | 64601 | ▇▅▅▂▁ |
| tenure_no_ceodb | 0 | 1 | 1.03 | 0.16 | 1 | 1.0 | 1.0 | 1.00 | 3 | ▇▁▁▁▁ |
| max_tenure_ceodb | 0 | 1 | 1.05 | 0.23 | 1 | 1.0 | 1.0 | 1.00 | 4 | ▇▁▁▁▁ |
| fyear_gone | 0 | 1 | 2006.54 | 13.69 | 1980 | 2000.0 | 2006.0 | 2013.00 | 2997 | ▇▁▁▁▁ |
Explore Data
# Bar plot for CEO Dismissal
data_clean %>%
ggplot(aes(ceo_dismissal)) +
geom_bar() +
labs(title = "CEO Dismissal Count", x = "CEO Dismissal", y = "Count")
data_clean %>%
ggplot(aes(x = ceo_dismissal, y = tenure_no_ceodb)) +
geom_boxplot() +
labs(title = "CEO Dismissal vs. Tenure", x = "CEO Dismissal", y = "CEO Tenure")
data_clean <- data_clean %>%
drop_na(notes)
# Step 1: Binarize the data
data_binarized <- data_clean %>%
binarize()
data_binarized %>% glimpse()## Rows: 7,461
## Columns: 29
## $ `fyear__-Inf_1999` <dbl> …
## $ fyear__1999_2006 <dbl> …
## $ fyear__2006_2012 <dbl> …
## $ fyear__2012_Inf <dbl> …
## $ `co_per_rol__-Inf_6978` <dbl> …
## $ co_per_rol__6978_18259 <dbl> …
## $ co_per_rol__18259_33390 <dbl> …
## $ co_per_rol__33390_Inf <dbl> …
## $ departure_code__1 <dbl> …
## $ departure_code__2 <dbl> …
## $ departure_code__3 <dbl> …
## $ departure_code__4 <dbl> …
## $ departure_code__5 <dbl> …
## $ departure_code__6 <dbl> …
## $ departure_code__7 <dbl> …
## $ ceo_dismissal__0 <dbl> …
## $ ceo_dismissal__1 <dbl> …
## $ tenure_no_ceodb__1 <dbl> …
## $ tenure_no_ceodb__2 <dbl> …
## $ `tenure_no_ceodb__-OTHER` <dbl> …
## $ max_tenure_ceodb__1 <dbl> …
## $ max_tenure_ceodb__2 <dbl> …
## $ `max_tenure_ceodb__-OTHER` <dbl> …
## $ `fyear_gone__-Inf_2000` <dbl> …
## $ fyear_gone__2000_2006 <dbl> …
## $ fyear_gone__2006_2013 <dbl> …
## $ fyear_gone__2013_Inf <dbl> …
## $ `notes__Constantine_S._Macricostas_is_Chairman_of_the_Board_and_founder_of_the_Company._Mr._Macricostas_was_Executive_Chairman_of_the_Company_until_January_20,_2018._Mr._Macricostas_previously_served_as_Chief_Executive_Officer_of_the_Company_on_three_different_occasions_from_1974_until_August_1997,_from_February_2004_to_June_2005,_and_from_April_2009_until_May_2015._Mr._Macricostas_is_a_former_director_of_RagingWire_Data_Centers,_Inc.,_(“RagingWire”)._Mr._Macricostas_is_the_father_of_George_Macricostas._Mr._Macricostas’_knowledge_of_the_Company_and_its_operations,_as_well_as,_the_industry_is_invaluable_to_the_Board_of_Directors_in_evaluating_and_directing_the_Company’s_future._Through_his_long_service_to_the_Company_and_experience_in_the_photomask_industry,_he_has_developed_extensive_knowledge_in_the_areas_of_leadership,_safety,_risk_oversight,_management,_and_corporate_governance,_each_of_which_provides_great_value_to_the_Board_of_Directors._Mr._Macricostas_is_a_member_of_the_Cyber_Security_Committee_of_the_Board.` <dbl> …
## $ `notes__-OTHER` <dbl> …# Correlation for both categories of ceo_dismissal
data_correlation_0 <- data_binarized %>%
correlate(`ceo_dismissal__0`) # Correlation for one class
data_correlation_other <- data_binarized %>%
correlate(`ceo_dismissal__1`) # Correlation for the other class
# Display the correlation results
data_correlation_0## # A tibble: 29 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 ceo_dismissal 0 1
## 2 ceo_dismissal 1 -1
## 3 departure_code 3 -0.929
## 4 departure_code 5 0.477
## 5 departure_code 7 0.304
## 6 departure_code 4 -0.273
## 7 departure_code 6 0.0788
## 8 fyear -Inf_1999 0.0782
## 9 co_per_rol -Inf_6978 0.0596
## 10 fyear_gone -Inf_2000 0.0591
## # ℹ 19 more rowsdata_correlation_other## # A tibble: 29 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 ceo_dismissal 0 -1
## 2 ceo_dismissal 1 1
## 3 departure_code 3 0.929
## 4 departure_code 5 -0.477
## 5 departure_code 7 -0.304
## 6 departure_code 4 0.273
## 7 departure_code 6 -0.0788
## 8 fyear -Inf_1999 -0.0782
## 9 co_per_rol -Inf_6978 -0.0596
## 10 fyear_gone -Inf_2000 -0.0591
## # ℹ 19 more rows# Step 3: Plot the correlation funnel
data_correlation_0 %>%
correlationfunnel::plot_correlation_funnel() +
labs(title = "Correlation Funnel for CEO Dismissal (Class 0)")
data_correlation_other %>%
correlationfunnel::plot_correlation_funnel() +
labs(title = "Correlation Funnel for CEO Dismissal (Class Other)")
Model Building
Split Data
library(tidymodels)
# Set seed for reproducibility
set.seed(1234)
# Split the data into training and testing sets
data_split <- initial_split(data_clean, strata = ceo_dismissal)
data_train <- training(data_split)
data_test <- testing(data_split)
# Create cross-validation sets for the training data
data_cv <- vfold_cv(data_train, strata = ceo_dismissal)
data_cv## # 10-fold cross-validation using stratification
## # A tibble: 10 × 2
## splits id
## <list> <chr>
## 1 <split [5034/561]> Fold01
## 2 <split [5034/561]> Fold02
## 3 <split [5035/560]> Fold03
## 4 <split [5036/559]> Fold04
## 5 <split [5036/559]> Fold05
## 6 <split [5036/559]> Fold06
## 7 <split [5036/559]> Fold07
## 8 <split [5036/559]> Fold08
## 9 <split [5036/559]> Fold09
## 10 <split [5036/559]> Fold10Preprocess data
library(themis)
#data_train <- data_train %>%
#mutate(leftofc = as.Date(as.character(leftofc), format = "%Y-%m-%d"))
xgboost_rec <- recipes::recipe(ceo_dismissal ~ ., data = data_train) %>%
step_dummy(all_nominal_predictors(), -notes) %>%
step_tokenize(notes) %>%
step_tokenfilter(notes, max_tokens = 100) %>%
step_tfidf(notes) %>%
#step_date(leftofc, features = c("year", "month", "doy")) %>%
#step_mutate(leftofc_month = as.numeric(leftofc_month)) %>%
#step_select(-leftofc) %>%
step_smote(ceo_dismissal)
xgboost_rec %>% prep() %>% juice() %>% glimpse()## Rows: 8,966
## Columns: 114
## $ fyear <dbl> 1997, 1998, 2001, 1997, 1997, 1993, 2007, 1993…
## $ co_per_rol <dbl> 1, 6, 11, 16, 22, 33, 34, 43, 60, 66, 68, 71, …
## $ tenure_no_ceodb <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ max_tenure_ceodb <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ fyear_gone <dbl> 1998, 1998, 2002, 1997, 1998, 1995, 2007, 1993…
## $ departure_code_X2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ departure_code_X3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ departure_code_X4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ departure_code_X5 <dbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1…
## $ departure_code_X6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…
## $ departure_code_X7 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…
## $ departure_code_X8 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ departure_code_X9 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_notes_1 <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_1997 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_notes_1998 <dbl> 0.00000000, 0.00000000, 0.00000000, 0.19367862…
## $ tfidf_notes_1999 <dbl> 0.0000000, 0.1188082, 0.0000000, 0.0000000, 0.…
## $ tfidf_notes_a <dbl> 0.00000000, 0.04092227, 0.00000000, 0.00000000…
## $ tfidf_notes_acquisition <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_after <dbl> 0.07456141, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_agreement <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.…
## $ tfidf_notes_also <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_notes_an <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_and <dbl> 0.03621827, 0.10412753, 0.10412753, 0.05553468…
## $ tfidf_notes_announced <dbl> 0.07328246, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_april <dbl> 0.00000000, 0.00000000, 0.16949346, 0.00000000…
## $ tfidf_notes_as <dbl> 0.00000000, 0.00000000, 0.05955360, 0.12704769…
## $ tfidf_notes_at <dbl> 0.00000000, 0.07159414, 0.00000000, 0.00000000…
## $ tfidf_notes_based <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_notes_be <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_been <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_billion <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_board <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_business <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_notes_but <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_notes_by <dbl> 0.00000000, 0.07115023, 0.00000000, 0.00000000…
## $ tfidf_notes_ceo <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_chairman <dbl> 0.05129030, 0.04915320, 0.14745960, 0.07864512…
## $ tfidf_notes_chief <dbl> 0.05185503, 0.04969441, 0.07454161, 0.07951105…
## $ tfidf_notes_co <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_company <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ `tfidf_notes_company's` <dbl> 0.10256568, 0.09829211, 0.00000000, 0.00000000…
## $ tfidf_notes_corp <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_corporation <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.…
## $ tfidf_notes_december <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_director <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_directors <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_down <dbl> 0.00000000, 0.00000000, 0.12388487, 0.00000000…
## $ tfidf_notes_effective <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_notes_executive <dbl> 0.04908427, 0.04703909, 0.07055863, 0.07526254…
## $ tfidf_notes_financial <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_notes_following <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.…
## $ tfidf_notes_for <dbl> 0.00000000, 0.05607810, 0.00000000, 0.08972496…
## $ tfidf_notes_from <dbl> 0.00000000, 0.05609376, 0.00000000, 0.00000000…
## $ tfidf_notes_group <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.…
## $ tfidf_notes_had <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_has <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_have <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_notes_he <dbl> 0.05600000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_his <dbl> 0.00000000, 0.00000000, 0.00000000, 0.09445415…
## $ tfidf_notes_in <dbl> 0.04290549, 0.04111776, 0.12335328, 0.00000000…
## $ tfidf_notes_inc <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_into <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.…
## $ tfidf_notes_is <dbl> 0.00000000, 0.00000000, 0.10635525, 0.00000000…
## $ tfidf_notes_it <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_its <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_january <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_notes_july <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.…
## $ tfidf_notes_june <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.…
## $ tfidf_notes_march <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_may <dbl> 0.1110278, 0.0000000, 0.0000000, 0.0000000, 0.…
## $ tfidf_notes_merger <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_million <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_more <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_notes_mr <dbl> 0.06692910, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_named <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.…
## $ tfidf_notes_new <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_not <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_notes_of <dbl> 0.07027907, 0.06735077, 0.05051308, 0.00000000…
## $ tfidf_notes_officer <dbl> 0.00000000, 0.00000000, 0.08210740, 0.00000000…
## $ tfidf_notes_on <dbl> 0.05791901, 0.00000000, 0.00000000, 0.08880914…
## $ tfidf_notes_operating <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_over <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_position <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_president <dbl> 0.00000000, 0.00000000, 0.08309839, 0.08863828…
## $ tfidf_notes_resigned <dbl> 0.0000000, 0.0000000, 0.0000000, 0.1501982, 0.…
## $ tfidf_notes_retire <dbl> 0.09754320, 0.09347890, 0.00000000, 0.00000000…
## $ tfidf_notes_retired <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_retirement <dbl> 0.00000000, 0.00000000, 0.00000000, 0.15789109…
## $ tfidf_notes_role <dbl> 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0…
## $ tfidf_notes_said <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_served <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_share <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_notes_since <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_notes_stepped <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.…
## $ tfidf_notes_stock <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_notes_that <dbl> 0.05968588, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_the <dbl> 0.16607733, 0.15915744, 0.00000000, 0.00000000…
## $ tfidf_notes_this <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.…
## $ tfidf_notes_time <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.…
## $ tfidf_notes_to <dbl> 0.00000000, 0.00000000, 0.05843937, 0.00000000…
## $ tfidf_notes_today <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.…
## $ tfidf_notes_until <dbl> 0.00000000, 0.00000000, 0.00000000, 0.14938704…
## $ tfidf_notes_vice <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_was <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_when <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_which <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.…
## $ tfidf_notes_who <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_will <dbl> 0.00000000, 0.06472499, 0.00000000, 0.10355998…
## $ tfidf_notes_with <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_would <dbl> 0.1229135, 0.0000000, 0.0000000, 0.0000000, 0.…
## $ tfidf_notes_year <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000…
## $ tfidf_notes_years <dbl> 0.08768202, 0.08402860, 0.00000000, 0.00000000…
## $ ceo_dismissal <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…Specify Model
xgboost_spec <-
boost_tree(trees = tune(),
tree_depth = tune(),
min_n = tune(),
learn_rate = tune()) %>%
set_mode("classification") %>%
set_engine("xgboost")
xgboost_workflow <-
workflow() %>%
add_recipe(xgboost_rec) %>%
add_model(xgboost_spec) Tune hyperparameters
doParallel::registerDoParallel()
set.seed(65743)
xgboost_tune <-
tune_grid(xgboost_workflow,
resamples = data_cv,
grid = 10)## Warning: package 'xgboost' was built under R version 4.3.3xgboost_tune %>% collect_metrics()## # A tibble: 30 × 10
## trees min_n tree_depth learn_rate .metric .estimator mean n std_err
## <int> <int> <int> <dbl> <chr> <chr> <dbl> <int> <dbl>
## 1 1870 5 9 0.0481 accuracy binary 0.997 10 7.75e-4
## 2 1870 5 9 0.0481 brier_cla… binary 0.00276 10 5.83e-4
## 3 1870 5 9 0.0481 roc_auc binary 1.00 10 4.55e-5
## 4 963 7 14 0.291 accuracy binary 0.997 10 6.78e-4
## 5 963 7 14 0.291 brier_cla… binary 0.00270 10 5.70e-4
## 6 963 7 14 0.291 roc_auc binary 1.00 10 6.12e-5
## 7 1077 11 10 0.00312 accuracy binary 0.996 10 6.95e-4
## 8 1077 11 10 0.00312 brier_cla… binary 0.00339 10 5.52e-4
## 9 1077 11 10 0.00312 roc_auc binary 1.00 10 4.93e-5
## 10 643 14 11 0.00413 accuracy binary 0.996 10 6.95e-4
## # ℹ 20 more rows
## # ℹ 1 more variable: .config <chr>