Feature Hashing: Efficient Categorical Data Encoding for Large-Scale ML Systems

what feature hashing is, why it’s needed, and its advantages

Feature hashing is a powerful technique used to convert large, high-dimensional categorical data into a smaller, manageable numerical form. It’s especially useful in machine learning when dealing with data like text or categories that have millions of unique values. Instead of creating massive one-hot encodings, feature hashing maps features to a fixed-size vector using a hash function, reducing memory usage and speeding up model training. While it’s scalable and efficient, it can introduce some noise due to hash collisions.