# Gdro Tabular Imbalance Eval

> Assesses deep learning models' ability to classify highly imbalanced binary tabular data by comparing standard empirical risk minimization against group distributionally robust optimization. Use when the user wants to benchmark on Multiple benchmark imbalanced tabular datasets, or asks about evaluating this task. Reports g-mean.

- Skill: `qhjqhj00/gdro-tabular-imbalance-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/gdro-tabular-imbalance-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/gdro-tabular-imbalance-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/gdro-tabular-imbalance-eval

---


# gdro-tabular-imbalance-eval

> Investigating Group Distributionally Robust Optimization for Deep Imbalanced Learning: A Case Study of Binary Tabular Data Classification — Ismail B. Mustapha et al. (2023) (arXiv:2303.02505, 2023)

## What this evaluates

Assesses deep learning models' ability to classify highly imbalanced binary tabular data by comparing standard empirical risk minimization against group distributionally robust optimization.

## Datasets

- **Multiple benchmark imbalanced tabular datasets** — total ?; splits: train (-1), validation (-1)

## Metrics

- `g-mean` **(primary)** — range: [0, 1]
  - Geometric mean of sensitivity and specificity, balancing performance across majority and minority classes.
- `ROC-AUC` — range: [0, 1]
  - Area under the Receiver Operating Characteristic curve, measuring the model's ability to discriminate between classes across all classification thresholds.

## Input / output format

**Input**: Tabular feature vectors representing binary classification instances.

**Output**: Binary class predictions or class probabilities used to compute g-mean and ROC-AUC.

## Scoring recipe

```python
metrics_per_run = []
for rep in range(5):
    model = train(method, train_split)
    preds = model.predict(val_split)
    g_mean = geometric_mean(sensitivity(preds), specificity(preds))
    roc_auc = compute_roc_auc(preds, labels)
    metrics_per_run.append((g_mean, roc_auc))
mean_g_mean = np.mean([m[0] for m in metrics_per_run])
std_g_mean = np.std([m[0] for m in metrics_per_run])
# Repeat for ROC-AUC
# Rank methods per dataset per metric, then average ranks across datasets
```

## Common pitfalls

- Relying solely on overall accuracy, which is misleading for imbalanced data.
- Reporting single-run results instead of the required 5-repetition mean ± standard deviation.
- Averaging performance across datasets without tracking per-dataset rankings, which obscures method consistency.

## Evidence (verbatim from paper)

> Experiments on benchmark imbalanced tabular datasets show that gDRO outperforms ERM and classical methods (e.g., reweighting) in g-mean and ROC-AUC metrics, demonstrating superior generalization to minority classes in highly skewed data. The mean (±standard deviation) of the validation performance of each method across the respective evaluation metrics for each dataset over five repetitions of the experiment are presented in Fig 2.

## Citation

```bibtex
@misc{mustapha2023gdro,
  title={Investigating Group Distributionally Robust Optimization for Deep Imbalanced Learning: A Case Study of Binary Tabular Data Classification},
  author={Ismail B. Mustapha et al. (2023)},
  year={2023},
  note={arXiv:2303.02505}
}
```

- arXiv: 2303.02505