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home / skills / jeremylongshore / claude-code-plugins-plus-skills / train-test-splitter

train-test-splitter skill

/skills/07-ml-training/train-test-splitter

This skill helps you design, validate, and generate production-ready train-test splitter configurations for ML training tasks.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill train-test-splitter

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

Files (1)
SKILL.md
2.1 KB
---
name: "train-test-splitter"
description: |
  Test train test splitter operations. Auto-activating skill for ML Training.
  Triggers on: train test splitter, train test splitter
  Part of the ML Training skill category. Use when writing or running tests. Trigger with phrases like "train test splitter", "train splitter", "train".
allowed-tools: "Read, Write, Edit, Bash(python:*), Bash(pip:*)"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Train Test Splitter

## Overview

This skill provides automated assistance for train test splitter tasks within the ML Training domain.

## When to Use

This skill activates automatically when you:
- Mention "train test splitter" in your request
- Ask about train test splitter 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 train test splitter
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 train test splitter"
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 train-test splitting workflows for machine learning training. It helps create reproducible splits, generate ready-to-run code snippets, and validate split configurations against common standards. Use it to speed data preparation and ensure consistent experiment setups.

How this skill works

The skill inspects dataset inputs and requested split parameters, then generates code and configuration for deterministic splits (random seed, stratification, ratio). It validates that required fields are present, checks compatibility with common ML libraries (scikit-learn, PyTorch, TensorFlow), and returns reproducible examples and tests you can run in a Jupyter environment.

When to use it

  • Preparing datasets for model training and evaluation
  • Writing unit or integration tests that require deterministic splits
  • Needing reproducible, production-ready split code snippets
  • Validating split configurations, stratification, or class balance
  • Generating CI-friendly data split routines for experiments

Best practices

  • Always set and document a random seed for reproducibility
  • Use stratified splits for imbalanced classification tasks
  • Validate class distribution in train and test sets after splitting
  • Keep splitting logic separate from model code for testability
  • Include small unit tests that assert split sizes and class ratios

Example use cases

  • Generate a scikit-learn train_test_split snippet with stratify and seed
  • Create a PyTorch Dataset split routine and corresponding unit tests
  • Validate a CSV dataset to ensure no label leakage between splits
  • Produce CI-ready configuration that enforces minimum test set size
  • Suggest remediation when requested split parameters are invalid

FAQ

What inputs does the skill need to generate a split?

Provide dataset shape or a sample, desired train/test ratios, whether to stratify, and an optional random seed.

Can it enforce class balance in splits?

Yes. The skill recommends and generates stratified splits and checks class distribution post-split, flagging large imbalances.

Which libraries are supported?

Common ML libraries like scikit-learn, PyTorch, and TensorFlow are supported through generated examples and compatible patterns.