The information gain model is a type of machine learning concept that can be used in place of the inverse document frequency approach. The concept being used here is the probability of observing two terms together on the basis of their occurrence in an index. We use an index to evaluate the occurrence of two terms `x`

and `y`

and calculate the information gain for each term in the index:

`P(x)`

: Probability of a term`x`

appearing in a listing`P(x|y)`

: Probability of the term`x`

appearing given a term`y`

also appears

The information gain value of the term `y`

can be computed as follows:

This equation says that ...

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