Hashing is a method used in machine learning for grouping categorical data, and it is especially useful when the entire variety of categories is huge, but only a subset of those categories occurs in the dataset.
For instance, there are around 73,000 different kinds of trees on Earth. The 73,000 different tree species might be divided into 73,000 different categories. On the other hand, assuming there are only two hundred species currently included in your data set, you might use hashing to classify trees into possibly 500 groups.
It’s possible to store many species of trees in the same bucket. The hashing process may group together genetically distinct species, such as the baobab and the red maple. Nonetheless, hashing remains a useful technique for partitioning huge classified collections into the required granularity. By algorithmically grouping data into hash values, hashing reduces the number of potential values for a category characteristic from a big number to a considerably smaller number.
Application of Hashing in the AI
Hashing is used in many different AI applications, such as:
Data storage and retrieval
In the context of databases and search indexes, hashing is often used to generate one-of-a-kind identities for data records. It is possible to produce a unique identifier, or hash code, for each record by hashing its primary attributes, such as name or ID, which then allows for more efficient storage and retrieval.
Password and data security
To ensure the safety of passwords or other sensitive data, hashing is often used to encrypt the data before it is stored or verified. It is practically impossible to decode the original data from a hash code since hash algorithms provide fixed-length hash values for each input.
Data deduplication
Hash tables may be used to find and eliminate duplicates in huge data sets, saving time during data processing and analysis. Data records may be compared for duplicates by calculating their hash values and comparing them.
Machine learning
Hashing is a key component of machine learning, where it is used to create feature vectors from raw data. Training and inference times may be slashed by employing hashes of the input data as features.
Information retrieval
A hash algorithm is used to develop index structures like hash tables, cuckoo filters, and bloom filters for information retrieval. By keeping a concise representation of vast data sets, these structures make it easy to search and retrieve them.
