 Simple heuristics like Tallying, which is a type of improper linear model  (section 2.3),  and Take-the-best  (section 7.2),  when tested against linear regression on many data sets, have been both found to outperform linear regression on out-of-sample prediction tasks, particularly when the training-sample size is low (Czerlinski et al. 1999; Rieskamp &amp; Dieckmann 2012).