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Feature hashing
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<p>In <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a>, feature hashing, also known as the hashing trick (by analogy to the <a href="/facts/Kernel_trick/fYuURIPk">kernel trick</a>), is a fast and space-efficient way of vectorizing <a href="/facts/Feature_(machine_learning)/nzAhYxfu">features</a>, i.e. turning arbitrary features into indices in a vector or matrix. It works by applying a <a href="/facts/Hash_function/rk6MlCt3">hash function</a> to the features and using their hash values as indices directly (after a modulo operation), rather than looking the indices up in an <a href="/facts/Associative_array/IFGd2xcP">associative array</a>. In addition to its use for encoding non-numeric values, feature hashing can also be used for <a href="/facts/Dimensionality_reduction/XoRpcrB0">dimensionality reduction</a>. </p><p>This trick is often attributed to Weinberger et al. (2009), but there exists a much earlier description of this method published by John Moody in 1989. </p>