Dataset Splitter

소개

Streamline the data preparation phase of machine learning projects by automatically dividing datasets into optimized subsets. This skill interprets user-defined proportions to generate and execute Python code that splits data into training, validation, and testing sets while maintaining data integrity. By implementing best practices like stratification for imbalanced classes and randomization to prevent bias, it ensures that your data is properly partitioned for robust model development and evaluation.

주요 기능

• Automated generation of Python splitting scripts using standard ML libraries
• 712 GitHub stars
• Implementation of stratified sampling to maintain class distributions
• Automatic execution and creation of subset data files
• Randomized data shuffling to eliminate selection bias
• Support for custom training, validation, and testing ratios

사용 사례

• Preparing raw CSV files for machine learning model training
• Creating validation sets for hyperparameter tuning
• Partitioning large datasets into standardized train-test splits