The 1R machine learning scheme is a very simple one that proves surprisingly effective on the standard datasets commonly used for evaluation. This paper describes the method and discusses two aspects of the algorithm that bear further analysis: the way that intervals are formed when discretizing continuously-valued attributes, and the treatment of missing values are treated. We then show how the algorithm can be extended to avoid a problem endemic to most practical machine learning algorithms -- their frequent dismissal of an attribute as irrelevant when in fact it is highly relevant when combined with other attributes.