This study aims to evaluate the robustness of parametric and nonparametric procedures using alternative definitions of validation data for loblolly pine. Specifically, four data division strategies were implemented: random selection of one-third of the trees in the data set, selection of the smallest one-third of the trees by diameter at breast height (DBH), selection of the middle third of the trees by DBH, and selection of the largest third of the trees by DBH. Results indicate that tree taper was predicted reasonably well by both procedures when the smallest, medium-sized, or randomly selected trees were withheld for validation. However, when the largest trees were withheld for validation, diameters predicted by the nonparametric random forest algorithm were considerably less accurate than those predicted by the parametric models, especially for diameters near the tree top. When extrapolation is anticipated, a carefully designed data-partitioning strategy should provide some protection against poor results for given prediction objectives.

中文翻译：

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树柄锥度参数化和非参数拟合程序的健壮性及验证数据的替代定义

本研究旨在使用火炬松的验证数据的替代定义来评估参数和非参数程序的鲁棒性。具体来说，实施了四种数据划分策略：随机选择数据集中三分之一的树木，按胸高（DBH）的直径选择最小的三分之一树木，选择中间三分之一的树木由DBH选择，然后由DBH选择最大的三分之一树木。结果表明，当保留最小，中型或随机选择的树木进行验证时，两种方法都可以很好地预测树木的锥度。但是，当保留最大的树进行验证时，非参数随机森林算法预测的直径比参数模型预测的直径准确度低得多，特别是对于树顶附近的直径。当预期外推时，精心设计的数据分区策略应为给定的预测目标提供一些保护，以防止不良结果。