Construct · 2 fields · 2 citations
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In statistical learning, a split is the partition of a dataset into separate portions (typically training, validation, and test sets) so that a model is fitted on one part and its performance estimated honestly on data it never saw, guarding against overfitting. Hastie, Tibshirani, and Friedman's The elements of statistical learning treats such data splitting and cross-validation as the basis for assessing generalisation error. The referent is a division of observations for model assessment, measured by the sizes and composition of the partitions, and it is unrelated to a layer of hide or a corporate share action.[1]
In corporate finance, a (stock) split is a company's subdivision of its existing shares into a larger number of lower-priced shares, so that an owner of one share at a high price ends up with several shares whose prices sum to roughly the same value, leaving total equity unchanged. Hull's Options, futures, and other derivatives explains how such splits are handled, including the proportional adjustment of option contracts to keep positions economically neutral. The referent is an accounting and market action on a firm's share structure, measured by the split ratio, and it shares only its name with a hide layer or a data partition.[2]
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