C4.5 works in ways similar to ID3, but uses the gain ratio as a partitioning criterion, which in part resolves the issue mentioned previously. Another advantage is that it accepts partition on numeric attributes, which it splits into categories. The value of the split is selected in order to decrease the entropy for the considered attribute. Other differences from ID3 are that C4.5 allows for post-pruning, which is basically the bottom up simplification of the tree to avoid overfitting to the training data.
Using the gain ratio as partitioning criterion overcoming a shortcomes of ID3, which is to prefer attributes with many modalities as nodes because they have a higher information gain. The gain ratio divides the information gain by a value called split information. This value is computed as minus the sum of: the ratio of the number of cases in each modality of the attribute divided by the number of cases to partition upon, multiplied by the base 2 logarithm of the number...
