Decision Trees involve a lot of splitting to achieve purity in the subsets. When the purity is highest, the prediction of the decision is the strongest. Information gain is a continuous calculative process of measuring the impurity at each subset before splitting the data further. Information gain uses Entropy to determine this purity. At each subgroup, the ratio of various variables in the subsets determines the amount of information required to choose the subset for splitting further. Information gain will be more balanced in the proportion of variables in the subset, promising more purity.