Can I specify custom split ratios?

Yes, you can define exact percentages for your training, validation, and testing sets, such as a 70/15/15 or 80/20 split.

Does it ensure data randomization?

Yes, following machine learning best practices, the skill ensures the splitting process is randomized to avoid bias in your training and testing sets.

What file formats are supported?

The skill is designed to work with common machine learning data formats, primarily focusing on CSV files and tabular data.

How does the skill split the data?

The skill analyzes your request and generates Python code using standard libraries to programmatically divide your files into specified subsets.

ML Dataset Partitioning Adapter

Name: ML Dataset Partitioning Adapter
Author: intent-solutions-io

byintent-solutions-io

0•

データサイエンスとML

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

This skill streamlines data preparation by automatically dividing datasets into optimized subsets for training, validation, and testing. It generates and executes Python code based on natural language requests, ensuring proper data ratios and maintaining integrity across common data formats like CSVs. By automating the boilerplate of train-test splitting, it allows data scientists and developers to focus on model evaluation and performance tuning within the Claude Code environment.

主な機能

01Automated train-test-validation splits

02Support for CSV and large dataset partitioning

03Randomized sampling to ensure unbiased subsets

040 GitHub stars

05Python code generation for data manipulation

06Custom proportion configuration and ratio logic

ユースケース

01Partitioning datasets to evaluate cross-model performance

02Creating validation sets for model hyperparameter tuning

03Preparing raw CSV data for neural network training

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add intent-solutions-io/plugins-nixtla skill-adapter

For use in Claude.ai and ChatGPT

Download Skill