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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