How does the skill ensure randomization?

The generated code utilizes industry-standard libraries like scikit-learn, incorporating shuffling and random seeds to avoid selection bias in your datasets.

Can it create three sets instead of just two?

Absolutely. You can partition your data into training, validation, and testing sets simultaneously by specifying the desired proportions for each.

What file formats are supported?

The skill is optimized for structured data commonly used in machine learning, primarily focusing on CSV files and dataframes within a Python environment.

Does this skill handle imbalanced datasets?

Yes, the skill follows best practices for stratification to ensure that class distributions are maintained correctly across all resulting subsets.

Can I specify the exact ratios for splitting?

Yes, you can request specific percentages such as 70/15/15 or 80/20, and the skill will generate the corresponding Python code to handle the split.

Dataset Splitter & ML Partitioning

Name: Dataset Splitter & ML Partitioning
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

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

About

The Splitting Datasets skill streamlines machine learning preparation by automatically dividing raw data into high-quality training, validation, and testing sets. By generating and executing Python code using standard ML libraries, it ensures proper data partitioning based on user-defined ratios. This skill is essential for developers and data scientists who need to maintain data integrity, implement stratification for imbalanced datasets, and ensure randomization to prevent model bias during the evaluation process.

Key Features

Automated Python code generation for ML data splitting
Customizable ratios for training, validation, and testing sets
3 GitHub stars
Support for CSV and large-scale dataset partitioning
Built-in data integrity and randomization checks
Optional stratification for handling imbalanced class distributions

Use Cases

Preparing structured data for model training and cross-validation
Creating robust evaluation sets to test model performance
Automating data pre-processing workflows within the Claude environment

About

Key Features

Automated Python code generation for ML data splitting
Customizable ratios for training, validation, and testing sets
3 GitHub stars
Support for CSV and large-scale dataset partitioning
Built-in data integrity and randomization checks
Optional stratification for handling imbalanced class distributions

Use Cases

Preparing structured data for model training and cross-validation
Creating robust evaluation sets to test model performance
Automating data pre-processing workflows within the Claude environment