Text Classification & Sentiment Analysis with Microsoft Azure: Feature Hashing for Dimensionality Reduction

The basic purpose of doing feature hashing is to reduce the dimensionality of the feature space. Feature hashing is a technique used to convert a large number of features into a smaller, fixed-size vector. This is achieved by using a hash function to map the features to a fixed number of indices in a hash table. The resulting vector contains only a subset of the original features, which reduces the memory requirements and computational complexity of the algorithm. Feature hashing is particularly useful when dealing with high-dimensional data, such as text data, where the number of features can be very large.