Norinder Ulf, Carlsson Lars, Boyer Scott, Eklund Martin
H. Lundbeck A/S, Ottiliavej 9, 2500 Valby, Denmark.
J Chem Inf Model. 2014 Jun 23;54(6):1596-603. doi: 10.1021/ci5001168. Epub 2014 May 21.
Conformal prediction is introduced as an alternative approach to domain applicability estimation. The advantages of using conformal prediction are as follows: First, the approach is based on a consistent and well-defined mathematical framework. Second, the understanding of the confidence level concept in conformal predictions is straightforward, e.g. a confidence level of 0.8 means that the conformal predictor will commit, at most, 20% errors (i.e., true values outside the assigned prediction range). Third, the confidence level can be varied depending on the situation where the model is to be applied and the consequences of such changes are readily understandable, i.e. prediction ranges are increased or decreased, and the changes can immediately be inspected. We demonstrate the usefulness of conformal prediction by applying it to 10 publicly available data sets.
共形预测作为一种领域适用性估计的替代方法被引入。使用共形预测的优点如下：首先，该方法基于一个一致且定义明确的数学框架。其次，对共形预测中置信水平概念的理解很直接，例如，0.8的置信水平意味着共形预测器最多会犯20%的错误（即真实值在指定预测范围之外）。第三，置信水平可以根据模型应用的情况而变化，并且这种变化的后果很容易理解，即预测范围会增加或减少，并且可以立即检查这些变化。我们通过将共形预测应用于10个公开可用的数据集来证明其有用性。
