---
name: biomedical-tabular-model-selection
description: Shortlist machine learning methods for high-dimensional biomedical tabular data using TabBench-Bio. Match modality, sample/feature budgets, metrics and compute constraints; account for uncertainty, failed fits and training-data overlap.
license: Apache-2.0
---

# Biomedical Tabular Model Selection — TabBench-Bio

Use current evidence from https://tabbench-bio.eu to shortlist machine learning methods
and recommend a local validation plan. Do not hard-code winners, scores or overlap flags.

## 1. Match the user's problem

Identify task, modality, labelled training samples, usable features, target metric,
grouping/imbalance, and compute constraints. Ask only for details that change the
shortlist; otherwise state assumptions. A task description and counts suffice.

Start with [llms.txt](https://tabbench-bio.eu/llms.txt). Use
[data reference](https://tabbench-bio.eu/skills/biomedical-tabular-model-selection/references/data.md)
for JSON endpoints, joins and an example query.
Record the snapshot date; disclose unavailable evidence rather than inventing it.

Choose matching `cell_options` and filter `domain_elo` by cell, domain and metric.
When an exact budget is unavailable, inspect neighbouring grid points and state
the mismatch. `null` means full/uncapped, never zero. Aggregate reference rankings
are not modality-specific evidence; use task-specific scores and metric directions
for regression or individual datasets. Report limited target coverage.

## 2. Compare candidates fairly

- Use **strict** results as primary evidence. Adaptive results may reuse a smaller
  sample budget after training OOM; label them as sensitivity results, not nominal
  measurements. Never mix views.
- Compare Elo, 95% intervals and `n_targets` at the same operating point. Elo is not
  accuracy, and individual intervals do not establish pairwise significance.
- Include a competitive simple baseline and a few complementary candidates.
  Compare `cost_grid` at the same cell/domain; its per-fold timings are not hardware
  guarantees or per-patient latency.
- Join `models[model_id]` for current flags. For each `training_data_overlap: true`,
  disclose: **Part of the benchmark training data was used in the training process
  of this model.** Prefer unflagged evidence for the main shortlist; discuss flagged
  models separately when relevant. A false flag does not prove absence of overlap.
- Check model version, `regular_max_features` and failed-fit coverage. Above-limit
  results may rely on modified adapters. Distinguish failed fits scored at chance
  from successful fits; missing results are not zero.
- Treat AutoGluon as the benchmark's separate one-hour AutoML reference. Respect
  `plot_excluded_models` when interpreting displayed rankings.

## 3. Recommend and validate

Return the task/modality, budgets, metric, analysis view and snapshot date, plus a
compact table of candidates, scores/uncertainty, cost and rationale. Cite the exact
artifacts used and separate measurements from inference. If relevant evidence is
missing, propose a baseline experiment instead of forcing a ranking.

Validate candidates on the user's data with common splits and metrics. Fit
preprocessing and feature selection within training folds; preserve patient,
batch or temporal grouping and keep final test data separate from tuning.
Benchmark evidence guides a shortlist, not a universal winner or permission to
start expensive experiments.

Cite [CITATION.cff](https://tabbench-bio.eu/CITATION.cff) or
[CITATION.bib](https://tabbench-bio.eu/CITATION.bib):
[arXiv:2609.07441](https://arxiv.org/abs/2609.07441),
[DOI](https://doi.org/10.48550/arXiv.2609.07441).