The importance of complexity in model selection
The importance of complexity in model selection
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DOI：
10.1006/jmps.1999.1283
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发表时间：
2000-03-01
影响因子：
1.8
通讯作者：
Myung, IJ
中科院分区：
文献类型：
--
作者：
Myung, IJ
模型选择不应仅仅基于拟合优度，还必须考虑模型的复杂性。而认知心理学中数学建模的目标是从一组相互竞争的模型中选择一个最能捕捉潜在心理过程的模型。选择最适合特定数据集的模型将无法实现这一目标。这是因为一个高度复杂的模型可以提供一个良好的照明，而不一定与他的底层过程有任何可解释的关系。它表明，模型选择的基础上，只适合观察到的数据将导致所有不必要的复杂模型的选择，过拟合的数据，从而概括性差。过拟合的影响首先要通过模型选择的方法加以适当的处理。最后给出了人工数据选择方法的应用实例。(C)北京大学出版社.
Model selection should be based not solely on goodness-of-fit, but must also consider model complexity. While the goal of mathematical modeling in cognitive psychology is to select one model from a set of competing models that best captures the underlying mental process. choosing the model that best fits a particular set of data will not achieve this goal. This is because a highly complex model can provide a good lit without necessarily bearing any interpretable relationship with he underlying process. it is shown that model selection based solely on the fit to observed data will result in the choice of all unnecessarily complex model that overfits the data, and thus generalizes poorly. The effect of over-fitting must be properly of first by model selection methods. An application example of selection methods using artificial data is also presented. (C) 2000 Academic Press.