Abstract：
The growing interest in chemoinformatic model uncertainty calls for a summary of the most widely used regression techniques and how to estimate their reliability. Regression models learn a mapping from the space of explanatory variables to the space of continuous output values. Among other limitations, the predictive performance of the model is restricted by the training data used for model fitting. Identification of unusual objects by outlier detection methods can improve model performance. Additionally, proper model evaluation necessitates defining the limitations of the model, often called the applicability domain. Comparable to certain classifiers, some regression techniques come with built-in methods or augmentations to quantify their (un)certainty, while others rely on generic procedures. The theoretical background of their working principles and how to deduce specific and general definitions for their domain of applicability shall be explained.
摘要：
对化学信息学模型不确定性的兴趣日益增加，要求对最广泛使用的回归技术以及如何估计其可靠性进行总结。回归模型学习从解释变量空间到连续输出值空间的映射。除其他限制外，模型的预测性能受到用于模型拟合的训练数据的限制。通过离群点检测方法识别异常对象可以提高模型性能。此外,正确的模型评估需要定义模型的局限性，通常被称为适用性领域。与某些分类器相比，一些回归技术带有内置的方法或增强来量化它们的(不)确定性，而其他人则依赖于通用程序。应解释其工作原理的理论背景以及如何为其适用范围推导特定和通用的定义。
