Splitswhere

Splitswhere is a lesser-known but innovative tool designed to help users analyze and visualize data splits in machine learning workflows. It provides a visual interface for examining how datasets are partitioned into training, validation, and test sets, which is crucial for ensuring balanced and representative splits. The tool is particularly useful for researchers and practitioners who need to verify that their data division adheres to intended criteria, such as maintaining class distribution or temporal consistency.

The primary function of Splitswhere is to generate interactive plots that display the distribution of samples across different splits. Users can upload their dataset and specify how it was divided, allowing the tool to highlight potential biases or inconsistencies. For instance, it can detect if certain classes or time periods are overrepresented in one split while underrepresented in another. This helps in identifying issues early in the machine learning pipeline, which can significantly impact model performance and reliability.

Splitswhere supports various data formats, including CSV, JSON, and Pandas DataFrames, making it accessible to a wide range of users. While it does not perform the actual splitting of data, it serves as a diagnostic tool to validate existing splits. The simplicity of its interface ensures that users without advanced technical expertise can still benefit from its insights. Overall, Splitswhere bridges the gap between data preparation and model training by promoting transparency and accountability in dataset partitioning.