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bio-machine-learning-model-validation

Run nested cross-validation with stratified splits for omics classifiers.

Updated Aug 27, 2026
Search Tags:#cross-validation#omics#biomedical#scikit-learn#hyperparameter-tuning#model-validation#nested-cv
One-click install
npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-machine-learning-model-validation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-machine-learning-model-validation
Source: https://github.com/stellaromics/fast-bioinfo/tree/main/.claude/agents/spatial-analysis/skills/bio-machine-learning-model-validation
Command: npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-machine-learning-model-validation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Nested cross-validation and stratified splits for unbiased evaluation of omics classifiers, helping prevent data leakage and overfitting in biomarker discovery.

Core Features & Use Cases

  • Nested cross-validation with outer and inner folds to obtain robust performance estimates while tuning hyperparameters on biomedical data.
  • Stratified K-Fold and group-aware splits to handle class imbalance and sample dependencies, with guidance to keep preprocessing inside the CV loop to prevent leakage.
  • Practical use cases include validating classifiers for biomarker discovery in omics datasets and optimizing hyperparameters for biomedical models.

Quick Start

Run nested cross-validation with 5 outer folds and 3 inner folds to obtain a robust estimate of model performance.

Frequently Asked Questions about bio-machine-learning-model-validation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I prevent data leakage during hyperparameter tuning for biomedical classifiers?▼

Prevent data leakage during hyperparameter tuning by using nested cross-validation, which isolates hyperparameter search within inner folds while evaluating performance on untouched outer folds.

What is nested cross-validation and when do I need it for omics data?▼

Nested cross-validation is a robust evaluation method using outer and inner folds to tune hyperparameters and estimate performance simultaneously. You need it for small, imbalanced biomedical datasets to avoid overfitting.

How do I run stratified k-fold cross-validation with scikit-learn for imbalanced classes?▼

Run stratified k-fold cross-validation by applying StratifiedKFold to maintain class proportions across folds, ensuring robust hyperparameter search and performance reporting for imbalanced biomedical datasets.

Can I use GridSearchCV inside a cross-validation pipeline for biomarker discovery?▼

Yes, you can use GridSearchCV inside an outer cross-validation pipeline. Keeping all preprocessing steps inside the CV loop ensures unbiased evaluation of omics classifiers during biomarker discovery.

Does nested cross-validation work with small sample sizes in biomedical datasets?▼

Yes, nested cross-validation works with small sample sizes in biomedical datasets. It uses stratified and group-aware splits to handle class imbalance and sample dependencies while providing robust performance estimates.

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