Dataset Partitioning

소개

Streamline machine learning model preparation by automatically partitioning raw data into optimized training, validation, and testing sets. This skill analyzes specific dataset requirements to generate and execute Python-based splitting logic, ensuring proper data integrity and randomization. It handles various distribution ratios and file formats, making it an essential tool for data scientists and developers looking to establish robust evaluation frameworks and prevent data leakage during model development.

주요 기능

• Implements stratification to maintain class distributions in imbalanced data
• Ensures data integrity and randomization to prevent model bias
• Automates the creation of separate output files for partitioned sets
• Supports customizable ratios for training, validation, and testing subsets
• Generates Python-based partitioning code using standard machine learning libraries
• 712 GitHub stars

사용 사례

• Creating dedicated validation sets for hyperparameter tuning
• Partitioning large datasets into standardized training and testing splits
• Preparing raw CSV data for machine learning model training