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home / skills / jeremylongshore / claude-code-plugins-plus-skills / cross-validation-setup

cross-validation-setup skill

/skills/07-ml-training/cross-validation-setup

This skill guides you through cross validation setup, generating production-ready configurations and best practices for ML training.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill cross-validation-setup

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
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---
name: "cross-validation-setup"
description: |
  Configure cross validation setup operations. Auto-activating skill for ML Training.
  Triggers on: cross validation setup, cross validation setup
  Part of the ML Training skill category. Use when working with cross validation setup functionality. Trigger with phrases like "cross validation setup", "cross setup", "cross".
allowed-tools: "Read, Write, Edit, Bash(python:*), Bash(pip:*)"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Cross Validation Setup

## Overview

This skill provides automated assistance for cross validation setup tasks within the ML Training domain.

## When to Use

This skill activates automatically when you:
- Mention "cross validation setup" in your request
- Ask about cross validation setup patterns or best practices
- Need help with machine learning training skills covering data preparation, model training, hyperparameter tuning, and experiment tracking.

## Instructions

1. Provides step-by-step guidance for cross validation setup
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. Validates outputs against common standards

## Examples

**Example: Basic Usage**
Request: "Help me with cross validation setup"
Result: Provides step-by-step guidance and generates appropriate configurations


## Prerequisites

- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of ml training concepts


## Output

- Generated configurations and code
- Best practice recommendations
- Validation results


## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |


## Resources

- Official documentation for related tools
- Best practices guides
- Community examples and tutorials

## Related Skills

Part of the **ML Training** skill category.
Tags: ml, training, pytorch, tensorflow, sklearn

Overview

This skill automates the setup of cross validation workflows for machine learning training. It guides data splits, fold generation, and integration with training, tuning, and experiment tracking. Use it to produce reproducible, production-ready configurations and code snippets tailored to your framework.

How this skill works

The skill inspects requested cross validation patterns and generates step-by-step setup instructions and configuration files (e.g., fold definitions, seeds, and stratification rules). It emits runnable code for common frameworks (scikit-learn, PyTorch, TensorFlow) and validates the configuration against common standards such as reproducibility and fold balance. It can also suggest tracking hooks and hyperparameter sweep integration points.

When to use it

  • You need to implement cross validation for model evaluation or selection.
  • Setting up stratified or grouped folds for imbalanced or grouped data.
  • Preparing reproducible train/validation/test splits for experiments.
  • Integrating cross validation with hyperparameter tuning pipelines.
  • Generating production-ready code or CI configurations for model training.

Best practices

  • Define and fix random seeds and fold indices to ensure reproducibility.
  • Use stratification or group-based folds when labels are imbalanced or samples are correlated.
  • Validate fold balance and leakage risk before training (check time-based leakage for temporal data).
  • Log fold assignments and metrics to experiment tracking for traceability.
  • Keep data preprocessing consistent across folds by fitting transformers only on training folds.

Example use cases

  • Create a stratified 5-fold cross validation setup for a binary classification dataset using scikit-learn.
  • Generate grouped time-aware folds for a user-session prediction task to prevent leakage.
  • Produce PyTorch DataLoader splits and seed-controlled samplers for reproducible training runs.
  • Output CI-ready configuration to run cross validation and aggregate metrics across folds.
  • Integrate cross validation with a hyperparameter sweep tool and generate code to collect per-fold scores.

FAQ

Can this skill produce code for different ML frameworks?

Yes. It generates framework-specific snippets (scikit-learn, PyTorch, TensorFlow) and can be adapted to other toolchains with minimal edits.

How does it prevent data leakage across folds?

It recommends and enforces patterns like group or time-based folding, fitting preprocessing only on training folds, and validating that no identifiers cross fold boundaries.