Data binning, also known as bucketing, is a data preprocessing technique used to group continuous data into discrete intervals or “bins.” This process simplifies the data by replacing individual values within a specified range with a single representative value, often enhancing the interpretability of the data. Binning is particularly useful in machine learning and data analysis, as it can improve model accuracy by reducing noise and addressing minor observation errors. Common methods for binning include binning by distance and binning by frequency, which help in transforming numeric data into categorical data or sampling values effectively.