current_annual_salary_usdOpenML #42125 ↗Employee Salaries
The Employee Salaries dataset provides compensation records for 9,228 active, permanent public sector employees in Montgomery County, MD. The primary predictive objective is tabular regression to estimate current annual salary based on departmental placement, job titles, tenure, and historical earnings.
Business Objective: Employee Salaries
01Business Context
Public sector human resources departments and enterprise compensation teams manage complex salary structures across diverse agencies, divisions, and union-negotiated pay bands. Ensuring fair compensation, standardizing career progressions, and budgeting across municipal departments requires accurate salary modeling.
02Analytical Objective
Predict the base current annual salary for municipal employees using demographic, positional, departmental, and historical tenure attributes.
03Economic & Decision Impact
Improves payroll forecasting accuracy, eliminates pay parity anomalies across agencies, and optimizes municipal budget allocations. Overestimating salaries leads to inflated budget reserves and fiscal waste, while underestimating leads to retention risks, labor disputes, and compensation non-compliance.
On this high-cardinality categorical dataset, gradient boosted decision trees like XGBoost and LightGBM typically achieve robust R² scores around 0.85-0.92 after extensive target encoding of position titles and divisions. In contrast, zero-shot tabular foundation models such as TabICL on Carla HQ infer contextual text embeddings and tabular patterns directly without manual feature preprocessing, delivering competitive out-of-the-box regression performance.
Target & Feature Column Definitions
Exhaustive business definitions, measurement units, roles, and target variables across all 13 columns.
| Col | Variable / Feature Name | Role | Type | Unit / Scale | Description & Business Meaning |
|---|---|---|---|---|---|
| A | current_annual_salary_usdCurrent Annual Salary | TARGET | NUM | USD | Target variable representing the standardized base annual salary contracted for the employee in USD. |
| B | full_nameEmployee Full Name | Feature | CAT | — | Full legal name of the county employee in 'Last, First Middle' format. |
| C | genderGender | Feature | CAT | — | Recorded biological sex or administrative gender of the employee ('F' for Female, 'M' for Male). |
| D | gross_pay_received_2016_usd2016 Gross Pay Received | LEAKAGE | NUM | USD | Total gross compensation paid to the employee during calendar year 2016, including base salary, overtime, and bonuses. TARGET LEAKAGE RISK(proxy target) Gross pay received during calendar year 2016 is an ex-post realization of compensation that directly incorporates base annual salary along with overtime and bonuses, serving as an immediate proxy for the target annual salary. • Excluded during model training & zero-shot inference to avoid artificially inflated metric scores. |
| D | department_codeDepartment Code | Feature | CAT | — | Standardized municipal department acronym or abbreviation (e.g., 'POL' for Police, 'HHS' for Health & Human Services, 'COR' for Correction & Rehabilitation). |
| E | overtime_pay_2016_usd2016 Overtime Pay | LEAKAGE | NUM | USD | Cumulative compensation paid specifically for overtime hours worked during calendar year 2016. TARGET LEAKAGE RISK(post event) Total overtime pay received is a post-event metric accumulated and recorded over the course of calendar year 2016, reflecting realized historical pay rather than pre-event base contracted compensation. • Excluded during model training & zero-shot inference to avoid artificially inflated metric scores. |
| E | department_nameDepartment Name | Feature | CAT | — | Full formal name of the municipal operating agency or government department. |
| F | division_nameDivision / Section | Feature | CAT | — | Specific internal operational division, bureau, or administrative unit within the department. |
| G | assignment_categoryEmployment Category | Feature | CAT | — | Employment status classification indicating full-time vs. part-time and regular vs. temporary status (e.g., 'Fulltime-Regular'). |
| H | position_titlePosition Title | Feature | CAT | — | Formal job title and grade level held by the employee (e.g., 'Master Police Officer', 'Social Worker IV'). |
| I | underfilled_job_titleUnderfilled Job Title | Feature | CAT | — | Lower classification title assigned when an employee is performing duties of a higher grade position while in training or trainee status. |
| J | date_first_hiredOriginal Hire Date | Feature | CAT | — | Calendar date on which the employee was first hired into county service in MM/DD/YYYY format. |
| K | year_first_hiredFirst Hired Year | Feature | NUM | years | Four-digit calendar year corresponding to the employee's initial appointment date, proxying organizational seniority. |
Interactive Data Table
Explore rows, feature values, and target labels for Employee Salaries.
TabICLv2 WebGPU Benchmark Results
Complete performance metrics on persisted IID or non-IID evaluation splits, executed 100% locally in the browser sandbox.
| Evaluation Split | Train Rows | Test Rows | Split Duration?Wall-clock inference time taken for this evaluation split running 100% locally via WebGPU.Learn more → | R² Score?Coefficient of determination measuring variance explained by the model.Learn more → | MAE?Mean Absolute Error in original target units, robust to outliers.Learn more → | RMSE?Root Mean Squared Error, penalizing large residual deviations.Learn more → | MAPE?Mean Absolute Percentage Error relative to true target values.Learn more → | RMSLE?Root Mean Squared Logarithmic Error, measuring proportional error scales.Learn more → |
|---|---|---|---|---|---|---|---|---|
| R1 / F1 | 6,152 | 3,076 | 36.77s | 0.9149 | 4,526.83 | 8,584.1 | — | — |
| R1 / F2 | 6,152 | 3,076 | 36.22s | 0.9204 | 4,244.48 | 8,244.49 | — | — |
| R1 / F3 | 6,152 | 3,076 | 36.35s | 0.9153 | 4,679.87 | 8,351.42 | — | — |
| R2 / F1 | 6,152 | 3,076 | 36.19s | 0.9264 | 4,177.77 | 7,762.72 | — | — |
| R2 / F2 | 6,152 | 3,076 | 36.13s | 0.9278 | 4,210.29 | 7,920.39 | — | — |
| R2 / F3 | 6,152 | 3,076 | 36.23s | 0.9212 | 4,203.37 | 8,210.76 | — | — |
| R3 / F1 | 6,152 | 3,076 | 36.11s | 0.9273 | 4,152.33 | 8,053.09 | — | — |
| R3 / F2 | 6,152 | 3,076 | 36.28s | 0.9197 | 4,512.09 | 8,434.66 | — | — |
| R3 / F3 | 6,152 | 3,076 | 36.21s | 0.9256 | 3,933.06 | 7,528.85 | — | — |
| Mean ± Std | — | — | 36.28s | 0.9221 ± 0.0047 | 4,293.3 | 8,121.2 | — | — |
Foundation Models vs Traditional ML Baselines
Side-by-side evaluation of our in-browser Carla engine (WebGPU), open-source tabular foundation models, and OpenML baselines.
| Rank | Algorithm / Model | Model Family | Runtime | R² Score?Coefficient of determination (R²) measuring proportion of variance explained.Google ML Guide & Details → | MAE?Mean Absolute Error measuring average prediction error magnitude.Learn more → | RMSE?Root Mean Squared Error penalizing large prediction errors.Learn more → | Reference / Repo |
|---|---|---|---|---|---|---|---|
| #1 | EXAONE Tabular | Tabular Foundation Model | PyTorch/Python | 0.9348 | 3,872.1 | 7,426.9 | LGAI-Research/EXAONE-Tabular ↗ |
| #2 | Streaming nanotabicl | Tabular Foundation Model | PyTorch/Python | 0.9267 | 4,148.7 | 7,874.3 | soda-inria/nanotabicl ↗ |
| #3 | nanotabicl Vanilla | Tabular Foundation Model | PyTorch/Python | 0.9221 | 4,246.2 | 8,108.9 | soda-inria/nanotabicl ↗ |
| #4 | Carla Engine (TabICLv2 WebGPU) | Tabular Foundation Model | Browser | 0.9221 | 4,293.3 | 8,121.2 | 100% In-Browser |
| #5 | Full TabICLv2 (PyTorch Reference) | Tabular Foundation Model | PyTorch/Python | 0.9218 | 4,253.5 | 8,125.8 | soda-inria/tabicl ↗ |
| #6 | TabPFN v3 | Tabular Foundation Model | PyTorch/Python | 0.9184 | 4,260.1 | 8,308 | PriorLabs/tabpfn ↗ |
| #7 | Google TabFM v1.0 | Tabular Foundation Model | PyTorch/Python | 0.8713 | 4,882.7 | 10,434.5 | google-research/tabfm ↗ |
Variable Schema & Summary Distributions
Observed numerical ranges, category cardinalities, missing rates, and sample values across 9,228 rows.
| Col | Variable Name | Type | Missing | Distinct | Summary Stats / Distribution | Sample Values |
|---|---|---|---|---|---|---|
| A | current_annual_salary_usdTARGET | NUM | 0 | 3,403 | [9196, 303091] μ=73390.2 σ=29117.5 | 69222.1897392.47104717.28 |
| B | full_name | CAT | 0 | 9,222 | — | Aarhus, Pam J.Aaron, David J.Aaron, Marsha M. |
| C | gender | CAT | 17 (0.18%) | 2 | 2 cats: M, F | FMF |
| D | gross_pay_received_2016_usdLEAKAGE | NUM | 100 (1.08%) | 8,977 | [0, 313700.42] μ=79504.0 σ=35103.3 | 71225.98103088.48107000.24 |
| D | department_code | CAT | 0 | 37 | 10 cats: POL, HHS, FRS +7 more | POLPOLHHS |
| E | overtime_pay_2016_usdLEAKAGE | NUM | 2,917 (31.61%) | 6,176 | [-2222.85, 125244.35] μ=10334.3 σ=12677.5 | 416.13326.191353.32 |
| E | department_name | CAT | 0 | 37 | 10 cats: Department of Police, Department of Health and Human Services, Fire and Rescue Services +7 more | Department of PoliceDepartment of PoliceDepartment of Health and Human Services |
| F | division_name | CAT | 0 | 694 | — | MSB Information Mgmt and Tech Division Records Management SectionISB Major Crimes Division Fugitive SectionAdult Protective and Case Management Services |
| G | assignment_category | CAT | 0 | 2 | 2 cats: Fulltime-Regular, Parttime-Regular | Fulltime-RegularFulltime-RegularFulltime-Regular |
| H | position_title | CAT | 0 | 385 | — | Office Services CoordinatorMaster Police OfficerSocial Worker IV |
| I | underfilled_job_title | CAT | 8,135 (88.16%) | 84 | — | Firefighter/Rescuer I (Recruit)Traffic Management Technician IPublic Safety Communications Specialist I |
| J | date_first_hired | CAT | 0 | 2,264 | — | 09/22/198609/12/198811/19/1989 |
| K | year_first_hired | NUM | 0 | 51 | [1965, 2016] μ=2003.6 σ=9.3 | 198619881989 |
Frequently Asked Questions: Employee Salaries
Common questions regarding the Employee Salaries dataset, machine learning task formulations, and in-browser tabular inference.
What is the Employee Salaries dataset used for?
The Employee Salaries dataset (OpenML ID 42125) is an authoritative public sector tabular benchmark used to evaluate regression algorithms predicting current annual base salaries from department, job title, and tenure metrics.
Can I run zero-server predictions on this dataset in Google Sheets?
Yes, Carla HQ enables zero-server in-context tabular prediction directly in spreadsheets using TabICL foundation models without requiring local Python environments or complex ML pipelines.
What machine learning models perform best on Employee Salaries?
Gradient Boosted Trees (LightGBM, XGBoost, CatBoost) perform well when high-cardinality titles and divisions are properly encoded. Zero-shot foundation models like TabICL achieve comparable accuracy instantly through in-context learning.
Test TabICLv2 on Employee Salaries Yourself
Open the pre-loaded Google Sheet and let Carla configure the target and task for local, zero-cloud tabular machine learning.
Launch Carla
Click once to open the spreadsheet and Carla side panel together.
Review the Setup
Carla selects the dataset target and task from this page automatically.
Evaluate & Predict
Run predictions and compute metrics with zero server uploads.
Provenance & Attribution
OpenML Dataset 42125