Logistic regression is a ubiquitous method for probabilistic classification.
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 closely matches logistic
regression in terms of classification error and log-loss, particularly for
high-dimensional data, while being significantly more computationally efficient
and having effectively no hyperparameters beyond regularisation. 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
computational expense.

给定一篇研究论文，从中提取出代表其主要主题和研究领域的五个关键词，并用一句简明扼要的中文句子对论文进行总结。