Instructions to use verosynthea/us-adult-income-baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use verosynthea/us-adult-income-baseline with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("verosynthea/us-adult-income-baseline", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
US Adult Income Baseline Classifier
A standard sklearn LogisticRegression trained on the UCI Adult Income dataset, predicting whether annual income exceeds $50,000 USD based on US Census features.
This model is hosted publicly as a reference target for fairness testing demonstrations, particularly with the verosynthea-validator. It is not intended for production use.
Training Data
UCI Adult Income dataset (1994 US Census extract). ~48,000 rows, 14 features. Loaded via the scikit-learn/adult-census-income HuggingFace dataset.
Features
| Feature | Type | Description |
|---|---|---|
age |
numeric | Age in years |
workclass |
categorical | Employment type (Private, Self-emp, Gov, ...) |
fnlwgt |
numeric | Census sampling weight |
education |
categorical | Highest education level |
education-num |
numeric | Education as ordinal number |
marital-status |
categorical | Marital status |
occupation |
categorical | Occupation category |
relationship |
categorical | Relationship to householder |
race |
categorical | Race |
sex |
categorical | Sex |
capital-gain |
numeric | Capital gains |
capital-loss |
numeric | Capital losses |
hours-per-week |
numeric | Hours worked per week |
native-country |
categorical | Country of birth |
Performance
- Accuracy on UCI Adult test set: ~84%
- Reproduces standard published baselines for this dataset
Intended Use
- Educational demonstrations of fairness testing
- Reference model for the verosynthea-validator tutorial
- Baseline comparator for fairness mitigation experiments
Out-of-Scope Use
- DO NOT use this model for any real income classification task. The training data is from 1994 US Census and is unsuitable for current or non-US populations.
- Running this model on Australian populations (e.g., via AUSynth data) produces large demographic bias gaps. This is by design — it demonstrates why models trained on one population should be tested before deployment in another.
Reproducing
pip install scikit-learn datasets pandas
python train_uci_adult_baseline.py
The training script is included in this repository.
Citation
Verosynthea (2026). US Adult Income Baseline.
https://verosynthea.com
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