In common with Section 4.1 we use leave-one-out cross-validation to assess predictive accuracy, again using bias, RMSPE and coverage probabilities to quantify prediction performance.
ERRIS applies a staged error model to reduce errors in hydrological simulations and to quantify prediction uncertainty.
Next, we expanded on previous applications of LiDAR by constructing 95% joint prediction confidence intervals to quantify prediction error at various spatial aggregations and habitat thresholds to determine a biologically and statistically meaningful grain size.
To quantify prediction accuracy, we use the Pearson's correlation coefficient between the true and the predicted phenotypes.
The area under ROC curve (AUC) has been widely used to quantify prediction performance using ROC analysis.
Importantly, the models quantify prediction uncertainty and aim at identifying key contributing factors and inter-species differences in their environmental niches.
To quantify prediction quality across years, we define the distance measure d = (Δ (prediction ) − Δ (minimal ) ) / (1 − Δ (minimal ) ) such that an optimal prediction has d = 0 and a random pick has d = 1.
Using a combination of newly collected and legacy soil data and a suite of spatial explanatory covariates, a total of 218 80 m resolution 3D soil attribute grids were produced using the digital mapping approach, together with quantified prediction uncertainties.
Further details regarding the computation of GBLUP and RKHS are provided in Appendix A. We quantified prediction accuracy using a replicated training testing evaluation.
