Dept of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy.
Institute of Biomedical Technologies - National Research Council, Segrate, MI, Italy.
PLoS One. 2023 May 18;18(5):e0285471. doi: 10.1371/journal.pone.0285471. eCollection 2023.
This methodological article is mainly aimed at establishing a bridge between classification and regression tasks, in a frame shaped by performance evaluation. More specifically, a general procedure for calculating performance measures is proposed, which can be applied to both classification and regression models. To this end, a notable change in the policy used to evaluate the confusion matrix is made, with the goal of reporting information about regression performance therein. This policy, called generalized token sharing, allows to a) assess models trained on both classification and regression tasks, b) evaluate the importance of input features, and c) inspect the behavior of multilayer perceptrons by looking at their hidden layers. The occurrence of success and failure patterns at the hidden layers of multilayer perceptrons trained and tested on selected regression problems, together with the effectiveness of layer-wise training, is also discussed.
这篇方法学文章主要旨在在性能评估的框架内，在分类和回归任务之间建立桥梁。更具体地说，提出了一种计算性能指标的通用程序，该程序可应用于分类和回归模型。为此，对用于评估混淆矩阵的策略进行了显著更改，目的是在其中报告有关回归性能的信息。该策略称为广义标记共享，允许 a）评估在分类和回归任务上训练的模型，b）评估输入特征的重要性，以及 c）通过查看多层感知器的隐藏层来检查其行为。还讨论了在选定的回归问题上训练和测试的多层感知器的隐藏层中成功和失败模式的出现，以及分层训练的有效性。
