FSinR：用于特征选择的详尽软件包
FSinR: an exhaustive package for feature selection
摘要：
特征选择是机器学习中的一项关键任务。它包括为模型构建或数据分析选择一些相关变量。我们介绍了R包FSinR，它实现了各种众所周知的过滤器和包装器方法，以及搜索算法。因此，该包提供了执行特征选择过程的可能性，该过程包括对特征子集的引导搜索与返回那些子集的评估度量的过滤器或包装器方法的组合。在本文中，我们还提供了一些使用该包的示例，并与R中包含特征选择方法的其他包进行了比较。
英文摘要：
Feature Selection (FS) is a key task in Machine Learning. It consists in selecting a number of relevant variables for the model construction or data analysis. We present the R package, FSinR, which implements a variety of widely known filter and wrapper methods, as well as search algorithms. Thus, the package provides the possibility to perform the feature selection process, which consists in the combination of a guided search on the subsets of features with the filter or wrapper methods that return an evaluation measure of those subsets. In this article, we also present some examples on the usage of the package and a comparison with other packages available in R that contain methods for feature selection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
