What algorithm is commonly used to convert text data into a numerical format suitable for machine learning?

Term Frequency - Inverse Document Frequency (TfIdf) is a widely adopted method for converting text data into a numerical format suitable for machine learning. The primary purpose of TfIdf is to quantify the importance of a word in a document relative to a collection of documents (also known as a corpus). It achieves this by combining two key components: term frequency, which measures how often a word appears in a document, and inverse document frequency, which assesses how unique or rare a word is across the whole corpus.

By calculating the product of these two components, TfIdf ensures that the representation highlights significant words that may carry more meaning while downweighting common words that contribute little to the uniqueness of the text. This creates a numerical representation (vector) of text that can be effectively used as input for various machine learning algorithms.

Furthermore, TfIdf is especially useful for tasks such as information retrieval, text classification, and clustering, as it allows machine learning models to more effectively understand the context and content of the data. The methodology accounts for both the relevance of the individual terms in the specific document and the broader context of the term's usage across multiple documents.