In the scientist’s house are many mansions... Outsiders often regard science as a sober enterprise, but we who are inside see it as the most romantic… - Pat Langley

As aims to address larger, more complex tasks, the problem of focusing on the most relevant information in a potentially overwhelming quantity of data has become increasingly important.

In recent years, researchers have made considerable progress on the of inductive learning tasks, but for theoretical results to have impact on practice, they must deal with the average case. In this paper we present an average-case analysis of a simple algorithm that induces one-level decision trees for concepts defined by a single relevant attribute. Given knowledge about the number of training instances, the number of irrelevant attributes, the amount of class and attribute noise, and the class and attribute distributions, we derive the expected classification accuracy over the entire instance space. We then examine the predictions of this analysis for different settings of these domain parameters, comparing them to experimental results to check our reasoning.

New heuristic (1) is used to prefer revision to premises that support relatively weak generalized beliefs.