bio-machine-learning-model-validation
Implements nested cross-validation and stratified splits for unbiased model evaluation on biomedical datasets. Prevents data leakage and overfitting in biomarker discovery. Use when validating classifiers or optimizing hyperparameters on omics data.
npx skills add BioTender-max/awesome-bio-agent-skills --skill model-validation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: numpy 1.26+, scikit-learn 1.4+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Cross-Validation for Biomedical Data **"Properly validate my omics classifier"** → Use nested cross-validation with stratified splits to get unbiased performance estimates while tuning hyperparameters on small biomedical datasets. - Python: `sklearn.model_selection.cross_val_score()` with `StratifiedKFold` inner/outer loops ## Why Nested CV Matters Simple train/test splits overestimate performance on small omics datasets. Nested CV provides unbiased estimates by separating hyperparameter tuning from performance evaluation. ## Nested Cross-Validation **Goal:** Obtain unbiased performance estimates by separating hyperparameter tuning from evaluation. **Approach:** Use an outer CV loop for scoring and an inner CV loop for grid search, preventing information leakage between tuning an
- Version Compatibility
- Why Nested CV Matters
- Nested Cross-Validation
- Stratified K-Fold
- Repeated Stratified K-Fold
- Leave-One-Out (Small Datasets)
- Group-Aware Splits
- CV Strategy Selection
- Avoiding Data Leakage
- Related Skills
What does the bio-machine-learning-model-validation skill do?
Implements nested cross-validation and stratified splits for unbiased model evaluation on biomedical datasets. Prevents data leakage and overfitting in biomarker discovery. Use when validating classifiers or optimizing hyperparameters on omics data.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill model-validation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.