# Bag-of-words model

Bag-of-words model is a method used to preprocess and vectorize text data, in order to create numeric representations that can be analyzed by machine learning algorithms.

The way it works is that first, the text data is split into individual words (or tokens), and then a matrix is created where each word is represented by a row, and each column represents a document. In each cell, the value represents how often that word appears in that document.

This approach is simple and efficient, but it has several disadvantages. The most important one is that it does not take into account the order of the words, which can be important for some applications. In addition, common words (such as “the”, “a”, “is”, etc.) will tend to dominate the matrix, and more rare words may be ignored altogether.

## Overcoming Bag-of-words Model Limitations

N-grams and Bag-of-words model

One way to overcome the disadvantage of bag-of-words model is to use n-grams. N-grams are simply sequences of n tokens, where n can be any number.

For example, if we have the following text: “I like to eat apples”, we can create the following bigrams: “I like”, “like to”, “to eat”, and “eat apples”.

Bigrams are simply two-token sequences, but we can create trigrams (three-token sequences), 4-grams, 5-grams, and so on.

The advantage of using n-grams is that they preserve the order of the words, which can be important for some applications.

Atf-idf and Bag-of-words

Another way to overcome the disadvantages of bag-of-words model is to use atf-idf weighting. Atf-idf (inverse document frequency) is a numerical statistic that is used to reflect how important a word is to a document.

The atf-idf value increases proportionally to the number of times a word appears in the document, but it is offset by the frequency of the word in the corpus, which helps to adjust for the fact that some words are more common than others.

The advantage of using atf-idf weighting is that it can help to reduce the effects of common words, while still taking into account the frequency of rarer words.

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