# Difference between Information Gain and Mutual Information for feature selection

What is the difference between information gain and mutual information?

At this point, I understand that information gain is calculated between a random variable and target class for classification while mutual information is calculated between two random variables.

Does mutual information become the same as information when it is calculated between a random variable and target class?

Important to note is while mutual information measures only the positive features, information gain measures both, negative as well as positive features of our data. For mathematical completeness, information gain of X given Y is given by IG(X|Y) = H(x) - H(X|Y), and mutual information I(X;Y) is given by I(X;Y) = sum_x sum_y P(X,Y) log {P(X,Y)/P(X)P(Y)}.