Can I specify custom split ratios?

Yes, you can request specific percentages such as 70/15/15, 80/20, or any other ratio required for your specific project.

Can it handle very large datasets?

Yes, it generates efficient code designed to handle various dataset sizes, utilizing the file system via Claude Code's Bash and Write tools.

Which file formats does the Dataset Splitter support?

The skill primarily targets common data formats like CSV and can be extended to other structured data formats supported by Python's data science libraries.

How does it ensure the data is split fairly?

The skill follows machine learning best practices by using randomization during the split to avoid ordering bias and ensure representative subsets.

Does this skill require external Python libraries?

The skill generates Python code that typically utilizes standard libraries like Scikit-Learn or Pandas to perform the partitioning efficiently.

Dataset Splitter

Name: Dataset Splitter
Author: jeremylongshore

byjeremylongshore

•

884

数据科学与机器学习

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

关于

This skill streamlines the data preparation phase of machine learning projects by automatically dividing raw datasets into optimized subsets. It analyzes user requirements for split ratios, generates production-ready Python code using standard libraries, and executes the partitioning to ensure data integrity. By handling randomization automatically, it helps developers prevent data leakage and selection bias, facilitating more robust model evaluation and faster ML experiment iteration directly within the Claude Code environment.

主要功能

Generation and execution of Python-based data processing scripts
Built-in randomization to prevent selection bias
Data integrity verification during the splitting process
Automated train-test-validation set partitioning
Support for custom split ratios (e.g., 70/15/15 or 80/20)
884 GitHub stars

使用场景

Preparing raw CSV data for supervised machine learning model training
Creating dedicated validation sets for iterative hyperparameter tuning
Establishing consistent hold-out test sets for final model performance evaluation

关于

主要功能

Generation and execution of Python-based data processing scripts
Built-in randomization to prevent selection bias
Data integrity verification during the splitting process
Automated train-test-validation set partitioning
Support for custom split ratios (e.g., 70/15/15 or 80/20)
884 GitHub stars

使用场景

Preparing raw CSV data for supervised machine learning model training
Creating dedicated validation sets for iterative hyperparameter tuning
Establishing consistent hold-out test sets for final model performance evaluation