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- W2040443875 abstract "Five cross validation methods, the k-fold, leave one plot out (LOP), leave one tree per plot out (LOT), 0.632 and 0.632+ bootstrap methods, were examined in this study for their suitability for performance evaluation of seven nonlinear mixed models based on a height–diameter relationship. The k-fold, LOP, 0.632 and 0.632+ methods used plot as the basic unit for data resampling, and applies to situations where predictions are needed for all trees in a new plot not used for model development. All four methods were suitable for evaluating the predictive performance of the selected model(s), and the 0.632 and 0.632+ methods were better than the k-fold and LOP methods. The LOT method used tree as the basic unit for data resampling, and applies to situations where predictions are needed for a portion of trees in a plot not used for model development, while the remaining trees of the plot are used for model development. The LOT method was not suitable for performance evaluation of the selected model(s)." @default.
- W2040443875 created "2016-06-24" @default.
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- W2040443875 date "2014-07-02" @default.
- W2040443875 modified "2023-10-14" @default.
- W2040443875 title "Suitability of five cross validation methods for performance evaluation of nonlinear mixed-effects forest models - a case study" @default.
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- W2040443875 doi "https://doi.org/10.1093/forestry/cpu025" @default.
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