Building a category predictor
A category predictor is used to predict the category to which a given piece of text belongs. This is frequently used in text classification to categorize text documents. Search engines frequently use this tool to order search results by relevance. For example, let's say that we want to predict whether a given sentence belongs to sports, politics, or science. To do this, we build a corpus of data and train an algorithm. This algorithm can then be used for inference on unknown data.
In order to build this predictor, we will use a metric called Term Frequency – Inverse Document Frequency (tf-idf). In a set of documents, we need to understand the importance of each word. The tf-idf metric helps us to understand how important a given word is to a document in a set of documents.
Let's consider the first part of this metric. The Term Frequency (tf) is basically a measure of how frequently each word appears in a given document. Since different...
