A Partition-Based Framework for Building and Validating Regression Models
A Partition-Based Framework for Building and Validating Regression Models
复制标题
用于构建和验证回归模型的基于分区的框架
DOI：
--
复制
发表时间：
2013
影响因子：
5.2
通讯作者：
H. Piringer
中科院分区：
文献类型：
--
作者：
T. Mühlbacher;H. Piringer
回归模型在基于一个或多个自变量分析或预测定量因变量的许多应用领域中起着关键作用。用于构建回归模型的自动化方法通常受限于在选择输入变量的过程（也称为特征子集选择）中结合领域知识。其他限制包括局部结构的识别，转换和变量之间的相互作用。本文的贡献是建立回归模型解决这些限制的框架。该框架结合了定性分析的关系结构的可视化和量化的相关性排名的任何数量的功能和功能，这可能是分类或连续的对。一个中心的方面是局部近似的条件目标分布的1D和2D特征域划分成不相交的区域。这使得能够对局部模式进行视觉调查，并在很大程度上避免了定量排名的结构性假设。我们描述了框架如何支持模型构建中的不同任务（例如，验证和比较），我们提出了一个交互式的工作流程特征子集的选择。一个真实世界的案例研究说明了逐步识别天然气消费的五维模型。我们还报告了领域专家在能源领域部署两个月后的反馈，表明构建和改进回归模型的工作量大大减少。
Regression models play a key role in many application domains for analyzing or predicting a quantitative dependent variable based on one or more independent variables. Automated approaches for building regression models are typically limited with respect to incorporating domain knowledge in the process of selecting input variables (also known as feature subset selection). Other limitations include the identification of local structures, transformations, and interactions between variables. The contribution of this paper is a framework for building regression models addressing these limitations. The framework combines a qualitative analysis of relationship structures by visualization and a quantification of relevance for ranking any number of features and pairs of features which may be categorical or continuous. A central aspect is the local approximation of the conditional target distribution by partitioning 1D and 2D feature domains into disjoint regions. This enables a visual investigation of local patterns and largely avoids structural assumptions for the quantitative ranking. We describe how the framework supports different tasks in model building (e.g., validation and comparison), and we present an interactive workflow for feature subset selection. A real-world case study illustrates the step-wise identification of a five-dimensional model for natural gas consumption. We also report feedback from domain experts after two months of deployment in the energy sector, indicating a significant effort reduction for building and improving regression models.