Feature hashing

When we have a lot of words and we want to count how many times each word appears, we can create a big list and mark the count of each word in it. However, if we have many words, the list will be very large, and it can cause issues like taking up a lot of memory space, making it slower and computationally expensive.

To solve this problem, we can use feature hashing, which is like putting all the words into a small box or bag that keeps them together. Then, we can label each box with a unique number, and whenever we see a word, we put it into its corresponding box. This way, we don't need to create a big list, and we can save memory and use it to work faster.

For example, imagine we have two words, "dog" and "cat." We put them in a box and label the box with a unique number, say 1. When we see the word "dog," we put it in the box labeled 1, and when we see "cat," we do the same. We don't need to keep track of each word's count, just put them in their respective box.

This technique is often used in natural language processing (NLP) for text classification, where we need to convert text data into a numeric format that machine learning algorithms can understand. By using feature hashing, we can represent text data efficiently and reduce the memory size needed to process text data.