# Lakemlb Eval

> lakemlb-eval

- Skill: `qhjqhj00/lakemlb-eval` (Agent Skill)
- Install (CLI): `npx skillmds@latest add qhjqhj00/lakemlb-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/lakemlb-eval/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/qhjqhj00/lakemlb-eval

---


# lakemlb-eval

> LakeMLB: Data Lake Machine Learning Benchmark — Pan et al. (2026) (arXiv:2602.10441, 2026)

## What this evaluates

Evaluates tabular machine learning models in data lake environments by leveraging auxiliary tables to improve prediction on a target table. It probes two integration paradigms: table unionability (vertical concatenation to increase training samples) and table joinability (horizontal enrichment to add features).

## Datasets

- **LakeMLB** — total ?; splits: Union (-1), Join (-1); repo https://github.com/zhengwang100/LakeMLB

## Metrics

- `predictive performance` **(primary)** — range: [0, 1]
  - Standard classification metrics (e.g., accuracy, F1-score) computed on the target table's label column. The exact metric is not specified in the provided section.

## Input / output format

**Input**: A target table with a specified label column to predict, and a single auxiliary table (either unionable or joinable) retrieved from the data lake.

**Output**: Predicted class labels for each row in the target table.

## Scoring recipe

```python
def score(predictions, gold_labels):
    correct = sum(1 for p, g in zip(predictions, gold_labels) if p == g)
    return correct / len(gold_labels)
```

## Common pitfalls

- Union tasks require semantic column alignment rather than exact name matching.
- Join tasks rely on fuzzy value overlap on join keys, which can cause alignment errors if not carefully validated.
- The benchmark assumes a single auxiliary table per task, though real data lakes may contain multiple.

## Evidence (verbatim from paper)

> The objective is to jointly leverage the target table and the auxiliary table to train a machine learning model that achieves improved predictive performance on the target table.

## Citation

```bibtex
@misc{pan2026lakemlb,
  title={LakeMLB: Data Lake Machine Learning Benchmark},
  author={Pan et al. (2026)},
  year={2026},
  note={arXiv:2602.10441}
}
```

- arXiv: 2602.10441