Learning through Changes: An Empirical Study of Dynamic Behaviors of Probability Estimation Trees Kun Zhang, Zujia Xu, Jing Peng, and Bill Buckles   View Paper (PDF)   Return to Session 16: Learning Techniques II Abstract In practice, learning from data is often hampered by the limited training examples. In this paper, as the size of training data varies, we empirically investigate several probability estimation tree algorithms over eighteen binary classification problems. Nine metrics are used to evaluate their performances. Our aggregated results show that ensemble trees consistently outperform single trees. Confusion factor trees(CFT) register poor calibration even as training size increases, which shows that CFTs are potentially biased if data sets have small noise. We also provide analysis on the observed performance of the tree algorithms. ©2006 Association for Computing Machinery