工具箱
计算机科学
块(置换群论)
空间分析
R包
自相关
数据挖掘
航程(航空)
选型
折叠(高阶函数)
计算科学
统计
数学
机器学习
工程类
几何学
航空航天工程
程序设计语言
作者
Roozbeh Valavi,Jane Elith,José Joaquín Lahoz‐Monfort,Gurutzeta Guillera‐Arroita
标识
DOI:10.1111/2041-210x.13107
摘要
Abstract When applied to structured data, conventional random cross‐validation techniques can lead to underestimation of prediction error, and may result in inappropriate model selection. We present the r package block CV , a new toolbox for cross‐validation of species distribution modelling. Although it has been developed with species distribution modelling in mind, it can be used for any spatial modelling. The package can generate spatially or environmentally separated folds. It includes tools to measure spatial autocorrelation ranges in candidate covariates, providing the user with insights into the spatial structure in these data. It also offers interactive graphical capabilities for creating spatial blocks and exploring data folds. Package block CV enables modellers to more easily implement a range of evaluation approaches. It will help the modelling community learn more about the impacts of evaluation approaches on our understanding of predictive performance of species distribution models.
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