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However, the effectiveness of logistic regression depends upon careful and relatively computationally expensive tuning, especially for the regularisation hyperparameter, and especially in the context of high-dimensional data. We present a prevalidated ridge regression model that in practice closely matches logistic regression in terms of 0\u20131\u00a0loss and log-loss, particularly for high-dimensional data, while being significantly more computationally efficient and having no user-tuned hyperparameters (the regularisation hyperparameter is learned automatically as part of the fitting process). We scale the coefficients of the model so as to minimise log-loss for a set of prevalidated predictions derived from the estimated leave-one-out cross-validation error. This exploits quantities already computed in the course of fitting the ridge regression model in order to find the scaling parameter with nominal additional computation.<\/jats:p>","DOI":"10.1007\/s10994-026-07059-1","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T11:17:42Z","timestamp":1782299862000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prevalidated Ridge Regression is a Highly-Efficient Drop-In Replacement for Logistic Regression for High-dimensional Data"],"prefix":"10.1007","volume":"115","author":[{"given":"Angus","family":"Dempster","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geoffrey I.","family":"Webb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel F.","family":"Schmidt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,24]]},"reference":[{"issue":"12","key":"7059_CR1","doi-asserted-by":"publisher","first-page":"3264","DOI":"10.1016\/j.patcog.2008.10.023","volume":"42","author":"MM Adankon","year":"2009","unstructured":"Adankon, M. 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