Lecture6 - C4.5

C4.5 enhances ID3 by making it more robust to noise, able to handle continuous attributes, deal with missing data, and convert decision trees to rules. It avoids overfitting through pre-pruning and post-pruning techniques. When dealing with continuous attributes, it evaluates all possible split points and chooses the optimal one. It treats missing data as a separate value but this is not always appropriate. It generates rules from trees in a greedy manner by pruning conditions to reduce estimated error. The next topic will be on instance-based classifiers.