预测性计算建模中准确性和可解释性的权衡。
Trade-off between accuracy and interpretability for predictive in silico modeling.
机构信息
School of Business & Informatics, University of Borås, SE-50190, Sweden.
出版信息
Future Med Chem. 2011 Apr;3(6):647-63. doi: 10.4155/fmc.11.23.
BACKGROUND
Accuracy concerns the ability of a model to make correct predictions, while interpretability concerns to what degree the model allows for human understanding. Models exhibiting the former property are many times more complex and opaque, while interpretable models may lack the necessary accuracy. The trade-off between accuracy and interpretability for predictive in silico modeling is investigated.
METHOD
A number of state-of-the-art methods for generating accurate models are compared with state-of-the-art methods for generating transparent models.
CONCLUSION
Results on 16 biopharmaceutical classification tasks demonstrate that, although the opaque methods generally obtain higher accuracies than the transparent ones, one often only has to pay a quite limited penalty in terms of predictive performance when choosing an interpretable model.
背景
准确性是指模型做出正确预测的能力，而可解释性是指模型允许人类理解的程度。前者通常具有更高的复杂性和不透明性，而可解释性模型可能缺乏必要的准确性。本研究旨在探讨预测性计算建模中准确性和可解释性之间的权衡。
方法
比较了生成准确模型的一系列最先进方法与生成透明模型的最先进方法。
结论
在 16 项生物制药分类任务上的结果表明，尽管不透明方法通常比透明方法获得更高的准确性，但在选择可解释模型时，通常只需要在预测性能方面付出相当有限的代价。
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