Assessing predictive models can be challenging. Modelers must navigate a wide array of evaluation methodologies implemented with incompatible interfaces across multiple packages which may give different or even contradictory results, while ensuring that their chosen approach properly estimates the performance of their model when generalizing to new observations. Assessing models fit to spatial data can be particularly difficult, given that model errors may exhibit spatial autocorrelation, model predictions are often aggregated to multiple spatial scales by end users, and models are often tasked with generalizing into spatial regions outside the boundaries of their initial training data. The waywiser package for the R language attempts to make assessing spatial models easier by providing an ergonomic toolkit for model evaluation tasks, with functions for multiple assessment methodologies sharing a unified interface. Functions from waywiser share standardized argument names and default values, making the user-facing interface simple and easy to learn. These functions are additionally designed to be easy to integrate into a wide variety of modeling workflows, accepting standard classes as inputs and returning size- and type-stable outputs, ensuring that their results are of consistent and predictable data types and dimensions. Additional features make it particularly easy to use waywiser along packages and workflows in the tidymodels ecosystem.

翻译：评估预测模型可能具有挑战性。建模者必须驾驭多种评估方法，这些方法通过多个包中不兼容的接口实现，可能给出不同甚至矛盾的结果，同时确保所选方法能在模型泛化到新观测时正确估计其性能。评估针对空间数据拟合的模型尤其困难，因为模型误差可能表现出空间自相关性，最终用户通常将模型预测聚合到多个空间尺度，且模型常常需要泛化到初始训练数据边界之外的空间区域。R语言的waywiser包通过提供用于模型评估任务的人体工学工具包，尝试使评估空间模型更容易，该工具包包含多种评估方法的函数，共享统一接口。waywiser的函数共享标准化的参数名称和默认值，使面向用户的接口简单易学。此外，这些函数设计便于集成到多种建模工作流程中，接受标准类作为输入，并返回大小和类型稳定的输出，确保其结果具有一致且可预测的数据类型和维度。额外功能使得waywiser特别容易与tidymodels生态系统中的包和工作流程结合使用。