Identifying Insufficient Data Coverage for Ordinal Continuous-Valued Attributes
Abolfazl Asudeh, Nima Shahbazi, Zhongjun Jin, H. V. Jagadish
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.
Topics & Concepts
Computer scienceOrdinal dataIntuitionOrdinal regressionData miningTraining setData modelingOrdinal optimizationArtificial intelligenceData setMachine learningDatabasePhilosophyEpistemologyMachine Learning and Data ClassificationData Mining Algorithms and ApplicationsMachine Learning and Algorithms