What are some examples of vectorization in machine learning?

What are some examples of vectorization in machine learning?

Vectorization in machine learning refers to the process of converting data into a numerical format that algorithms can process. Examples include converting text to vectors using techniques like TF-IDF or word embeddings.

Can you explain how TF-IDF works?

Sure! TF-IDF stands for Term Frequency-Inverse Document Frequency. It measures how important a word is to a document in a collection. It increases proportionally to the number of times a word appears in the document, but is offset by the frequency of the word in the entire collection.

What are word embeddings?

Word embeddings are a type of word representation that allows words to be represented as vectors in a continuous vector space. This captures semantic relationships between words, making it easier for models to understand context.

Can you give an example of a word embedding model?

One popular example of a word embedding model is Word2Vec, which uses neural networks to learn word associations from a large corpus of text.