How does the hashing trick work?

How does the hashing trick work?

It works by applying a hash function to the features and using their hash values as indices directly, rather than looking the indices up in an associative array. This trick is often attributed to Weinberger et al., but there exists a much earlier description of this method published by John Moody in 1989.

What is feature hashing in machine learning?

Feature hashing is a powerful technique for handling high-dimensional features in machine learning. It is fast, simple, memory-efficient, and well suited to online learning scenarios. Feature hashing has been made somewhat popular by libraries such as Vowpal Wabbit and scikit-learn.

How does feature hashing ( hashing-trick ) work?

Hash trick like other hash function store binary bits (index) which make large scale training feasible. In theory, more hashed length more performance gain, as illustrated in the original paper. It allocate origin feature into different bucket (finite length of feature space) so that their semantic get kept.

How does a feature vectorizer use a hashing trick?

Feature vectorization using hashing trick. Instead of maintaining a dictionary, a feature vectorizer that uses the hashing trick can build a vector of a pre-defined length by applying a hash function h to the features (e.g., words), then using the hash values directly as feature indices and updating the resulting vector at those indices.

How is the hashing of a key done?

When the value associated to a key is requested, the key is hashed giving an index in the array, then the corresponding list is scanned until the requested key is found, at that point the associated value is returned.

Do you need collision probabilities for hashing trick?

The documentation for standard hash functions typically include collision probabilities, so make sure to look those up when making your own hashing trick solution. Note that in some cases, you may even want collisions (e.g. for grouping similar legitimate words), in which case you may want to bucket those before hashing.