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The ML.BUCKETIZE function is specifically designed to segment continuous numerical data into discrete intervals, known as "buckets." This process enhances the interpretability of the data by transforming it into categorical labels or categories that represent ranges of values. When utilizing the function, it returns a string that corresponds to the name of the bucket in which a particular numerical value falls, effectively categorizing the data based on predefined intervals.

When data is bucketed, it facilitates various machine learning applications, such as simplifying models by reducing the complexity associated with continuous inputs or improving the performance of classifiers by grouping similar values together. This method is particularly useful in scenarios where continuous values have a significant range and can benefit from being divided into more manageable and meaningful segments that can help in pattern recognition and predictive modeling.