What file formats does this skill support?

The skill primarily handles common structured data formats like CSV files but can be used for any dataset readable via standard Python libraries like Pandas.

Can I specify custom split ratios?

Yes, you can define specific percentages for training, validation, and testing subsets (e.g., 80% training and 20% testing) to fit your specific project needs.

Can I use this for imbalanced datasets?

The skill follows best practices and can implement stratification to maintain consistent class distributions across all your partitioned subsets.

Does this skill execute code locally?

Yes, the skill generates and executes Python code within your environment to perform the data manipulation on your local files.

Does it handle data randomization?

Yes, the skill ensures the splitting process is randomized to avoid selection bias and ensure that your subsets are representative of the overall data.

Machine Learning Dataset Splitter

Name: Machine Learning Dataset Splitter
Author: jeremylongshore

byjeremylongshore

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883

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데이터 과학 및 ML

Partitions raw datasets into training, validation, and testing sets to streamline machine learning model development.

This skill empowers Claude to automate the critical data preparation phase of machine learning projects by programmatically dividing datasets into optimized subsets. It handles CSV and other structured data formats, generating and executing Python code to create precise train-test-validation splits based on user-defined ratios. By automating the partitioning process and ensuring proper randomization and stratification, it helps developers maintain rigorous evaluation standards and accelerate the model development lifecycle directly within the Claude Code environment.

주요 기능

01Automated train-validation-test partitioning

02Python-based execution using industry-standard libraries

03Support for CSV and large-scale data file formats

04Randomized and stratified splitting to prevent evaluation bias

05883 GitHub stars

06Customizable subset ratios (e.g., 70/15/15 or 80/20)

사용 사례

01Creating stratified splits for imbalanced classification datasets

02Preparing raw CSV data for supervised model training and evaluation

03Automating data preprocessing pipelines within a terminal-based AI workflow

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills skill-adapter

For use in Claude.ai and ChatGPT

Download Skill