Often there is an uninterpretable model that is statistically as good as, if not better than, a successful interpretable model. Accordingly, if one restricts attention to interpretable models, then one may sacrifice predictive power or other desirable properties. A minimal condition for an interpretable, usually parametric, model to be better than another model is that the first should have smaller mean‐squared error or integrated mean‐squared error. We show through a series of examples that this is often not the case and give the asymptotic forms of a variety of interpretable, partially interpretable, and noninterpretable methods. We find techniques that combine aspects of both interpretability and noninterpretability in models seem to give the best results.

中文翻译：

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赞扬部分可解释的预测因素

通常存在一个无法解释的模型，该模型在统计上与成功的可解释模型一样好，甚至要好于成功的可解释模型。因此，如果将注意力集中在可解释的模型上，则可能会牺牲预测能力或其他期望的属性。对于一个可解释的，通常是参数化的模型要优于另一个模型的最小条件是，第一个模型应具有较小的均方误差或积分均方误差。我们通过一系列示例说明，情况往往并非如此，并给出了各种可解释，部分可解释和不可解释方法的渐近形式。我们发现在模型中结合了可解释性和不可解释性两个方面的技术似乎可以提供最佳结果。