Identifying Insufficient Data Coverage for Ordinal Continuous-Valued Attributes

Abstract

Appropriate training data is a requirement for building good machine-learned models. In this paper, we study the notion of coverage for ordinal and continuous-valued attributes, by formalizing the intuition that the learned model can accurately predict only at data points for which there are "enough" similar data points in the training data set.