What file formats does the Dataset Splitter support?

The skill primarily handles CSV files but can generate Python code to process various tabular data formats common in machine learning workflows, such as Parquet or Excel, depending on the environment's libraries.

Can I specify custom split ratios?

Absolutely. You can request specific percentages like a 70/15/15 split or a simple 80/20 train-test ratio, and the skill will generate the corresponding logic.

Does it handle imbalanced datasets?

Yes, the skill is designed to follow best practices for stratification, ensuring that class distributions are maintained across the training, validation, and test subsets.

Does this skill require external libraries?

The skill generates code that typically utilizes standard Python libraries like Scikit-Learn and Pandas. You should ensure these dependencies are installed in your working environment.

How does it ensure data integrity?

The skill generates code that verifies the splitting process maintains the original data's integrity, ensuring no data loss occurs during the partitioning process.

Dataset Splitter

Name: Dataset Splitter
Author: jeremylongshore

byjeremylongshore

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897

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数据科学与机器学习

Automates the partitioning of datasets into training, validation, and testing sets for machine learning model development.

This skill streamlines the data preparation phase of machine learning workflows by automatically dividing raw datasets into optimized subsets. It handles complex partitioning tasks like train-test-validation splits, ensuring data integrity through proper randomization and optional stratification for imbalanced datasets. By generating and executing specialized Python code using industry-standard libraries, it eliminates manual data handling errors and accelerates the transition from data collection to model training within the Claude Code environment.

主要功能

01Direct generation and execution of Python-based data processing code

02Built-in randomization to prevent selection bias in subsets

03897 GitHub stars

04Support for custom split ratios and user-defined proportions

05Stratification capabilities to maintain class distributions

06Automated three-way splitting into training, validation, and testing sets

使用场景

01Creating dedicated hold-out test sets for final model performance evaluation

02Implementing stratified splits for imbalanced classification datasets

03Preparing raw CSV data for deep learning or neural network training

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills splitting-datasets

For use in Claude.ai and ChatGPT

Download Skill