Choose the incorrect statement.$$

Choose the incorrect statement.$$

In the bag of words, we take all unique words from the corpus, note the frequency of occurrence, and sort them in descending order.

The bag$-$of$-$words model is simple to understand and implement. It is a way of extracting features from the text for use in the machine$-$learning algorithm.

Vectorization in ML refers to the process of converting Natural Language Processing $($NLP$)$ text into string format.

In the bag$-$of$-$words, sentences are treated as a separate document, and we make a list of all words from all the documents, excluding punctuation.

Counting the number of times each word appears or calculating the frequency that each word appears in a document out of all the words are ways to convert text into vectors.