Year: 2016
Type: article
Abstract: Developing theories of when and why simple predictive models perform well is a key step in understanding decisions of cognitively bounded humans and intelligent machines.We are interested in how well simple models predict in regression.We list and review existing simple regression models and define new ones.We identify the lack of a large-scale empirical comparison of these models with state-of-the-art regression models in a predictive regression context.We report the results of such an empirical analysis on 60 real-world data sets.Simple regression models such as equal-weights regression routinely outperformed state-ofthe-art regression models, especially on small training-set sizes.There was no simple model that predicted well in all data sets, but in nearly all data sets, there was at least one simple model that predicted well.
Source: MPG.PuRe (Max Planck Society)
Authors: Jan Malte Lichtenberg, Özgür Şimşek
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2762814282