SplitSpoonSampler

SplitSpoonSampler is a Python library designed for efficient and flexible data sampling. It aims to simplify the process of splitting datasets into training, validation, and testing subsets, particularly in machine learning workflows. The library provides a user-friendly interface for various sampling strategies, catering to different data characteristics and research needs.

Key features of SplitSpoonSampler include its ability to handle stratified sampling, which is crucial for maintaining the distribution of class labels in imbalanced datasets. It also supports random sampling with options for shuffling the data before splitting. The library is designed to be compatible with common data structures like NumPy arrays and Pandas DataFrames, making integration into existing projects straightforward.

The primary goal of SplitSpoonSampler is to offer a reliable and reproducible method for dataset partitioning. By abstracting away the complexities of sampling algorithms, it allows data scientists and researchers to focus on model development and evaluation. The library's design emphasizes clarity and ease of use, aiming to reduce the overhead associated with data preparation. It can be particularly beneficial for beginners in machine learning who are learning to manage and split their datasets.