Random projections and Kernelised Leave One Cluster Out Cross-Validation: Universal baselines and evaluation tools for supervised machine learning for materials properties. (arXiv:2206.08841v1 [cs.LG])

With machine learning being a popular topic in current computational
materials science literature, creating representations for compounds has become
common place. These representations are rarely compared, as evaluating their
performance - and the performance of the algorithms that they are used with -
is non-trivial. With many materials datasets containing bias and skew caused by
the research process, leave one cluster out cross validation (LOCO-CV) has been
introduced as a way of measuring the performance of an algorithm in predicting …

arxiv cluster evaluation learning lg machine machine learning materials random supervised machine learning tools validation