Here are the slides I use for my course about the existing decision tree learning algorithms. Only the most popular ones are described: C4.5, CART and CHAID (a variant). The differences between these approaches are highlighted according: the splitting measure; the merging strategy during the splitting process; the approach for determining the right sized tree.
Keywords: machine learning, supervised methods, decision tree learning, classification tree, chaid, cart, c4.5
Slides: C4.5, CART and CHAID
References:
L. Breiman, J. Friedman, R. Olshen and C. Stone, “Classification and Regression Trees”, Wadsworth Int. Group, 1984.
G. Kass, “An exploratory technique for Investigating Large Quantities of Categorical Data”, Applied Statistics, 29(2), 1980, pp. 119-127.
R. Quinlan, “C4.5: Programs for machine learning”, Morgan Kaufman, 1993.
