Certain and Approximately Certain Models for Statistical Learning

arXiv:2402.17926v1 Announce Type: cross
Abstract: Real-world data is often incomplete and contains missing values. To train accurate models over real-world datasets, users need to spend a substantial amount of time and resources imputing and finding proper values for missing data items. In this paper, we demonstrate that it is possible to learn accurate models directly from data with missing values for certain training data and target models. We propose a unified approach for checking the necessity of data imputation to …

abstract arxiv cs.db cs.lg data datasets learn missing values paper resources spend statistical stat.ml train type values world