Dataset Splitter

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Streamline machine learning workflows by automating the essential step of dividing raw datasets into functional subsets. This skill analyzes user-specified ratios and file locations to generate and execute Python code that creates robust training, validation, and testing partitions. By incorporating best practices such as randomization and data integrity verification, it ensures that your data is properly prepared for unbiased model evaluation and training, allowing developers to move from raw data to model training faster.

Características Principales

• Automated generation of Python scripts for data partitioning
• Configurable ratios for training, validation, and testing sets
• Implementation of randomization to ensure unbiased subsets
• Data integrity verification to prevent loss or corruption during splitting
• Support for stratification to maintain class distributions in imbalanced data
• 706 GitHub stars

Casos de Uso

• Preparing raw CSV datasets for supervised machine learning model training
• Creating validation sets to tune model hyperparameters and prevent overfitting
• Partitioning large datasets into train-test splits for model performance evaluation