Predicting Annual Employee Performance Scores with XGBoost

XGBoost for Human Resource Management

Field: Human Resource ManagementLevel: Doctoral dissertation (S3)Method: XGBoost

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Using synthetic data for illustration, this analysis aims to build a boosting model for performance scores and evaluate prediction error. XGBoost is a gradient boosting algorithm that builds decision trees sequentially, each correcting the errors of the previous ones, with regularisation to prevent overfitting. It suits theses predicting a continuous outcome from many features with nonlinear relationships, compared against linear regression.

Predicting Annual Employee Performance Scores with XGBoost: result sheet

Project info

Field of studyHuman Resource Management
LevelDoctoral dissertation (S3)
Method typeMachine Learning
Estimated build time± 7 working days
Toolsxgboost::xgb.DMatrix, xgboost::xgb.cv, xgboost::xgb.train, xgboost::xgb.importance, ggplot2
PriceUS$34 - US$209

Research goal

Using synthetic data for illustration, this analysis aims to build a boosting model for performance scores and evaluate prediction error.

Variables

Role	Variable	Unit
Main	Annual performance score	score
Supporting 1	Training hours	hours
Supporting 2	Attendance rate	%

Example result

4.725RMSE (test)
3.825MAE (test)
0.752R2 (test)
0.619R2 linear (test)
5.861RMSE linear (test)
109Best nrounds

XGBoost with 109 boosting rounds (chosen by 5-fold cross-validation) predicts Annual performance score on the test set with R2 = 0.752, RMSE = 4.725 and MAE = 3.825, outperforming linear regression (R2 = 0.619). The feature with the largest gain is Training hours (50.0% of total gain).

Computed from illustrative synthetic data, not from a real study.

About XGBoost

XGBoost is a gradient boosting algorithm that builds decision trees sequentially, each correcting the errors of the previous ones, with regularisation to prevent overfitting. It suits theses predicting a continuous outcome from many features with nonlinear relationships, compared against linear regression.

Steps

1Prepare the feature matrix and split the data into 75% training and 25% test
2Convert the data to xgb.DMatrix format
3Set the parameters (eta, max_depth, subsample, colsample_bytree) and run 5-fold xgb.cv with early stopping
4Train the final model with xgb.train using the best number of rounds
5Compute test RMSE, MAE and R2 and compare with linear regression
6Interpret feature importance (gain) with xgb.importance and check the learning curve

Assumptions to check

Observations are independent and the test data represent future data
Features are numeric (categories must be encoded) with no leakage from the test data
Hyperparameters are tuned by cross-validation, not on the test data, to keep evaluation unbiased

Reading and reporting the results

Report the test RMSE, MAE and R2 with the selected number of rounds, then compare with the linear model to show the benefit of nonlinear modelling. Explain the features with the largest gain and use the learning curve to show the model is not overfitting.

Example R code

A short starting script with this project's variable names. The delivered version is adapted to your data and fully commented.

Show code

# Contoh kode R: XGBoost
suppressPackageStartupMessages({
  library(ggplot2)
  library(patchwork)
  library(xgboost)
})
set.seed(42)
# ---- Gaya grafik ----
pal <- c("#1f6feb", "#e8590c", "#2f9e44", "#ae3ec9", "#f59f00")
warna <- list(bg = "#f4f6fa", panel = "#ffffff", teks = "#1f2933", redup = "#5f6b78", grid = "#e3e8ee", bar = "#1d3557")
tata <- 1
theme_set(theme_minimal(base_size = 11) + theme(legend.position = "bottom"))

# ---- Data sintetis: Y kontinu dengan hubungan nonlinear dan interaksi ----
n <- max(200, 300)
d <- data.frame(training_hours = round(rnorm(n, 50, 10), 3),
                attendance_rate = round(rnorm(n, 30, 6), 3),
                fitur_3 = round(runif(n, 0, 10), 2),             # fitur pendukung (pengaruh ambang)
                fitur_4 = round(rnorm(n, 50, 12), 2),            # fitur pendukung (pengaruh lemah)
                fitur_5 = round(rnorm(n, 20, 5), 2))             # fitur pengganggu (tanpa pengaruh)
z1 <- as.numeric(scale(d$training_hours)); z2 <- as.numeric(scale(d$attendance_rate))
z4 <- as.numeric(scale(d$fitur_4))
a1 <- runif(1, 0.7, 1.1); a2 <- runif(1, 0.4, 0.7) * sample(c(-1, 1), 1)
f <- a1 * tanh(1.3 * z1) + a2 * z2 + runif(1, 0.2, 0.4) * z2^2 * sign(a2) +   # efek jenuh dan kuadratik
  runif(1, 0.3, 0.5) * (d$fitur_3 > 6) + 0.25 * z1 * z2 + 0.15 * z4           # efek ambang dan interaksi
f <- (f - mean(f)) / sd(f)
d$annual_performance_score <- round(100 + 15 * (f * runif(1, 0.85, 0.95) + rnorm(n, 0, runif(1, 0.3, 0.45))), 3)
write.csv(d, "data_contoh.csv", row.names = FALSE)

# ---- Bagi data latih 75% dan uji 25% ----
fitur <- c("training_hours", "attendance_rate", "fitur_3", "fitur_4", "fitur_5")
id_latih <- sample(n, round(0.75 * n))
latih <- d[id_latih, ]; uji <- d[-id_latih, ]
dlatih <- xgb.DMatrix(data = as.matrix(latih[, fitur]), label = latih$annual_performance_score)
duji <- xgb.DMatrix(data = as.matrix(uji[, fitur]), label = uji$annual_performance_score)

# ---- Validasi silang 5 lipatan untuk memilih jumlah iterasi (early stopping) ----
param <- list(objective = "reg:squarederror", eta = 0.05, max_depth = 4, subsample = 0.8,
              colsample_bytree = 0.8, min_child_weight = 3, nthread = 1,
              base_score = mean(latih$annual_performance_score))                           # titik awal = rata-rata Y latih
cv <- xgb.cv(params = param, data = dlatih, nrounds = 1000, nfold = 5, early_stopping_rounds = 30,
             metrics = "rmse", verbose = 0)
best <- cv$best_iteration
cat("Jumlah iterasi terbaik (CV):", best, "\n")

# ---- Model akhir dan evaluasi pada data uji ----
fit <- xgb.train(params = param, data = dlatih, nrounds = best, verbose = 0)
pred <- predict(fit, duji)
ukur <- function(y, p) c(RMSE = sqrt(mean((y - p)^2)), MAE = mean(abs(y - p)), R2 = 1 - sum((y - p)^2) / sum((y - mean(y))^2))
m_xgb <- ukur(uji$annual_performance_score, pred)
print(round(m_xgb, 4))

# ---- Pembanding: regresi linear berganda ----
fit_lm <- lm(annual_performance_score ~ training_hours + attendance_rate + fitur_3 + fitur_4 + fitur_5, data = latih)
pred_lm <- predict(fit_lm, uji)
m_lm <- ukur(uji$annual_performance_score, pred_lm)
print(rbind(XGBoost = m_xgb, Linear = m_lm))

# ---- Kepentingan fitur (gain) ----
imp <- xgb.importance(feature_names = fitur, model = fit)
print(imp)


# ---- Grafik ----
dp <- rbind(data.frame(aktual = uji$annual_performance_score, prediksi = pred_lm, model = "Linear"),
            data.frame(aktual = uji$annual_performance_score, prediksi = pred, model = "XGBoost"))
dp$model <- factor(dp$model, levels = c("Linear", "XGBoost"))
p1 <- ggplot(dp, aes(aktual, prediksi, colour = model)) +
  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = warna$redup) +
  geom_point(alpha = 0.6, size = 1.6) +
  scale_colour_manual(values = c(Linear = warna$redup, XGBoost = pal[1]), name = NULL) +
  labs(title = "Predicted vs actual (test set)", x = "Actual annual_performance_score", y = "Predicted annual_performance_score") +
  theme(legend.position = "bottom")
nama_tampil <- c("training_hours" = "training_hours", "attendance_rate" = "attendance_rate", fitur_3 = "Feature 3", fitur_4 = "Feature 4", fitur_5 = "Feature 5")
nama_tampil[] <- make.unique(nama_tampil, sep = " ")              # cegah label kembar
di <- data.frame(fitur = nama_tampil[imp$Feature], gain = imp$Gain)
di$fitur <- factor(di$fitur, levels = di$fitur[order(di$gain)])
p2 <- ggplot(di, aes(gain, fitur)) +
  geom_col(fill = pal[2], width = 0.65, alpha = 0.9) +
  geom_text(aes(label = sprintf("%.1f%%", 100 * gain)), hjust = -0.15, colour = warna$teks, size = 3) +
  scale_x_continuous(labels = function(v) paste0(100 * v, "%"), expand = expansion(mult = c(0, 0.2))) +
  labs(title = "Feature importance (gain)", x = "Share of total gain", y = NULL)
ev <- as.data.frame(cv$evaluation_log)
dl <- rbind(data.frame(iter = ev$iter, rmse = ev$train_rmse_mean, sd = ev$train_rmse_std, set = "CV train"),
            data.frame(iter = ev$iter, rmse = ev$test_rmse_mean, sd = ev$test_rmse_std, set = "CV validation"))
p3 <- ggplot(dl, aes(iter, rmse, colour = set, fill = set)) +
  geom_ribbon(aes(ymin = rmse - sd, ymax = rmse + sd), alpha = 0.15, colour = NA) +
  geom_line(linewidth = 1) +
  geom_vline(xintercept = best, linetype = "dashed", colour = warna$redup) +
  scale_colour_manual(values = c("CV train" = pal[3], "CV validation" = pal[4]), name = NULL) +
  scale_fill_manual(values = c("CV train" = pal[3], "CV validation" = pal[4]), name = NULL) +
  labs(title = sprintf("Learning curve (best = %d rounds)", best), x = "Boosting round", y = "RMSE") +
  theme(legend.position = "bottom")
p_hasil <- if (tata == 1) p1 | (p2 / p3) else (p2 / p3) | p1

print(p_hasil)

The project file you can download

The original project file is available. It is not just the picture above: you get the working R project. It can be downloaded after payment, and we send it by WhatsApp or email.

ZIP: analisis.R + data_contoh.csv + BACA-DULU.txt

What is inside the ZIP

The complete analysis script (analisis.R), commented step by step, ready to run in RStudio.
A sample dataset (data_contoh.csv) so the script runs straight away.
A short guide (BACA-DULU.txt): which packages to install, how to run it and how to swap in your own data.

Packages and prices

Choose how much help you need. Each higher package includes everything in the lower ones. Prices exclude 11% VAT.

Package 1

Code and Explanation

US$34

excl. 11% VAT

Duration: 7 working days

Output: R files + result figures

Deliverables

Fully commented R code (.R) for XGBoost
Example data and step-by-step run instructions
Ready-to-use result figures and metrics table
Short explanation of the method, its assumptions and how to read the results

Choose Package 1

Package 2 · most chosen

Your Data Analysis + Interpretation

US$94

excl. 11% VAT

Duration: 13 working days

Output: Code + analysis + interpretation notes

Includes all of Package 1, plus

Adapting the code to your own research dataset (CSV or Excel)
Assumption checks, result validation and data cleaning
Tables and figures formatted for your manuscript
Interpretation notes for the methods and discussion sections, for you to write up in your own words

Choose Package 2

Package 3

Full Guidance

US$209

excl. 11% VAT

Duration: 18-21 working days

Output: Code + analysis + guidance until ready to present

Includes all of Packages 1 & 2, plus

Comparison against one alternative method with the reasoning for the choice
Two 60-minute online coaching sessions so you fully understand the code and results
Revisions following your supervisor feedback (up to 3 rounds)
Practice Q&A to prepare for your seminar and defence
Full documentation (README, R package list (sessionInfo)) so the results can be reproduced

Choose Package 3

Payment: 50% down payment before work starts, balance after the deliverables are handed over
Tax: Prices exclude 11% VAT
Data and academic integrity: Results are computed from your own research data; you write and stand behind your manuscript, we help with the analysis and your understanding of it
Revisions: Up to 2 revisions are included in the price (Package 3: 3 rounds)

Frequently asked questions

Do I get the real project file?

Yes. The original project file is a ZIP containing the complete analisis.R script, a data_contoh.csv sample dataset and a BACA-DULU.txt guide. It can be downloaded after payment, and we send it by WhatsApp or email. For your own data (Package 2 and 3) the script is adapted to your dataset.

Is this suitable for a Doctoral dissertation (S3)?

Yes. XGBoost is commonly used at this level in Human Resource Management. We adapt the analysis to your research question, your own data and your supervisor's requirements.

Is the result sheet real data?

No. The sheet is computed from illustrative synthetic data so you can see the kind of output R Studio gives. For your order we run the same method on your own data.

Will I understand the code?

That is the point of the packages. Every script is commented, and Package 3 includes two coaching sessions plus practice questions so you can explain the method and the results at your seminar and defence.

How is the price decided?

Each project has three packages: Package 1 (code and explanation) US$34, Package 2 (your data analysis and interpretation) US$94 and Package 3 (full guidance) US$209, excluding 11% VAT. Larger datasets, extra methods or tight deadlines adjust the quote after a short consultation.

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The result sheet uses illustrative synthetic data. R is a programming language of the R Foundation project; RStudio and Posit are trademarks of Posit PBC.

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