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

데이터 과학 및 ML

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

소개

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.

주요 기능

Automated train-test-validation splits
Support for CSV and large dataset partitioning
Randomized sampling to ensure unbiased subsets
0 GitHub stars
Python code generation for data manipulation
Custom proportion configuration and ratio logic

사용 사례

Partitioning datasets to evaluate cross-model performance
Creating validation sets for model hyperparameter tuning
Preparing raw CSV data for neural network training

소개

주요 기능

Automated train-test-validation splits
Support for CSV and large dataset partitioning
Randomized sampling to ensure unbiased subsets
0 GitHub stars
Python code generation for data manipulation
Custom proportion configuration and ratio logic

사용 사례

Partitioning datasets to evaluate cross-model performance
Creating validation sets for model hyperparameter tuning
Preparing raw CSV data for neural network training