Binning Method

Binning, the process of grouping continuous data into discrete intervals, is a widely used technique across diverse fields, primarily aiming to improve data handling, model performance, and uncertainty quantification. Current research focuses on developing data-driven and adaptive binning methods, often integrated with machine learning models like tree-based structures and transformers, to optimize bin selection and mitigate overfitting, particularly in scenarios with limited data or complex data distributions. These advancements are impacting various applications, from improving the accuracy and reliability of machine learning predictions in diverse domains (e.g., medical imaging, crowd counting) to enabling faster and more efficient image recognition in specialized sensors.