Our empirical evaluations are based on both synthetic and real-world data sets. To create the synthetic data, we defined two concept classes and , and built all possible decision trees in these classes, many thousands of trees. We then created training and tests sets for all of these concepts. Our experiments only consider one level lookahead in this chapter. Although deeper lookahead might also be interesting, it is prohibitively expensive for the experimental design we employed.
Section describes related work on using lookahead to improve greedy search.
Section describes our experimental method. Sections and present the results with synthetic and real world data respectively. Section provides general conclusions.