This paper introduces different types of regression trees for viscosity property forecasting in polymer solutions. Although regression trees have been extensively used in other fields, they do not have been explored to predict the viscosity. One key issue in the context of materials science is to determine a priori which characteristics must be included to describe the prediction model due to a large number of molecular descriptors is obtained. To deal with this, we propose a wrapper method to select the features based on regression trees. Thus, we use regression trees to evaluate different subsets of attributes and build a model from the subset of features that achieved the minimum error. In particular, the performance of eight regression tree algorithms, including both linear and non-linear models, is evaluated and compared to other forecasting approaches using a dataset composed of 64 polymers and 2962 molecular descriptors. The results show that regression trees with nearest neighbors based local models in leaves predict with high accuracy. Moreover, results have been compared to other forecasting approaches such as multivariate linear regression, neural networks and support vector machines showing remarkable improvements in terms of accuracy.

中文翻译：

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使用回归树预测聚合物特性粘度的基于包装的特征选择

本文介绍了用于聚合物溶液粘度特性预测的不同类型的回归树。尽管回归树已广泛用于其他领域，但尚未探索它们来预测粘度。材料科学背景下的一个关键问题是先验确定必须包括哪些特征来描述预测模型，因为获得了大量的分子描述符。为了解决这个问题，我们提出了一种基于回归树的包装方法来选择特征。因此，我们使用回归树来评估不同的属性子集，并从实现最小误差的特征子集构建模型。特别是八种回归树算法的性能，包括线性和非线性模型，使用由 64 个聚合物和 2962 个分子描述符组成的数据集评估并与其他预测方法进行比较。结果表明，基于叶子中最近邻的局部模型的回归树预测精度很高。此外，结果与其他预测方法（如多元线性回归、神经网络和支持向量机）进行了比较，显示出在准确性方面的显着提高。