Why skewing works: learning difficult Boolean functions with greedy tree learners

We analyze skewing, an approach that has been empirically observed to enable greedy decision tree learners to learn "difficult" Boolean functions, such as parity, in the presence of irrelevant variables. We prove that, in an idealized setting, for any function and choice of skew parameters, skewing finds rele

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Bernard Rosell, Lisa Hellerstein, Soumya Ray, David Page
• Keywords: Greedy Decision Tree, ICML 2005, Machine Learning, Skew Parameters