Lakemlb Eval

---
name: lakemlb-eval
description: 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). Use when the user wants to benchmark on LakeMLB, or asks about evaluating this task. Reports predictive performance.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2602.10441
  bibtex_key: pan2026lakemlb
  confidence: medium
---

# 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