Decision trees are commonly used to solve classification problems, but creating efficient ones is a challenge. Different algorithms exist, but none are optimal for all data sets. A study compared traditional and newer decision tree techniques on real-world data sets. The evaluation metrics used were classification accuracy, tree depth, leaf nodes, and tree construction time.