What file formats does the ML Dataset Splitter support?

It is optimized for CSV files and standard tabular data formats commonly used in Python-based machine learning workflows.

How does it ensure the data split is truly random?

The generated code utilizes randomized shuffling techniques to prevent selection bias, ensuring that the resulting models generalize well to new data.

Can I specify custom split ratios for my dataset?

Yes, you can request any percentage distribution, such as 70% training, 15% validation, and 15% testing, and the skill will partition the files accordingly.

Does this skill handle imbalanced datasets?

The skill follows best practices for stratification to ensure that class distributions are maintained consistently across all generated subsets.

Do I need to write the Python splitting code myself?

No, the skill automatically generates and executes the necessary Python code based on your natural language instructions.

ML Dataset Splitter

Name: ML Dataset Splitter
Author: BbgnsurfTech

byBbgnsurfTech

•

Data Science & ML

Automates the partitioning of data into training, validation, and testing sets to streamline machine learning model development.

About

The ML Dataset Splitter skill simplifies the critical pre-processing step of data partitioning for machine learning projects. By interpreting natural language requests for specific split ratios (e.g., 70/15/15), it generates and executes Python-based scripts to organize datasets into distinct training, validation, and testing subsets. This tool ensures data integrity and consistency by implementing best practices like stratification for imbalanced datasets and randomization to prevent selection bias, making it an essential utility for data scientists and developers building robust AI models within the Claude Code environment.

Key Features

Support for CSV and large-scale dataset management
3 GitHub stars
Customizable data partitioning ratios for training, validation, and testing
Randomized shuffling to ensure model objectivity and prevent bias
Stratified splitting capabilities for handling imbalanced class distributions
Automated Python code generation and execution using standard ML libraries

Use Cases

Preparing raw CSV data for supervised learning projects with specific subset ratios
Creating quick 80/20 train-test splits for rapid model prototyping
Partitioning large datasets into three-way splits for deep learning cross-validation

About

Key Features

Support for CSV and large-scale dataset management
3 GitHub stars
Customizable data partitioning ratios for training, validation, and testing
Randomized shuffling to ensure model objectivity and prevent bias
Stratified splitting capabilities for handling imbalanced class distributions
Automated Python code generation and execution using standard ML libraries

Use Cases

Preparing raw CSV data for supervised learning projects with specific subset ratios
Creating quick 80/20 train-test splits for rapid model prototyping
Partitioning large datasets into three-way splits for deep learning cross-validation