When using tf-idf vectors, we expected that the cosine similarity measure would capture the similarity between documents, based on the overlap of terms between them. In a similar way, we would expect that a machine learning model, such as a classifier, would be able to learn weightings for individual terms; this would allow it to distinguish between documents from different classes. That is, it should be possible to learn a mapping between the presence (and weighting) of certain terms and a specific topic.
In the 20 Newsgroups example, each newsgroup topic is a class, and we can train a classifier using our tf-idf transformed vectors as input.
Since we are dealing with a ...
