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This means that model developers have to validate and evaluate their models' performance with regards to the effect of water stress on rice growth under a variety of environmental changes.
Many studies have attempted to identify factors that affect models' performance with only little success.
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It turned out, that the number of bootstrap replicates B has the least influence on model performance with higher B performing slightly better.
Therefore, even though performing well in this study, it would be informative to investigate the model performance with datasets collected from the wild.
When evaluating prediction models, test set data are typically used to assess model performance with unknowns.
References. 1. Reuschenbach P et al. ECOSAR model performance with a large test set of industrial chemicals.
Our preliminary results show a good model performance with R2 = 0.81.
Independent spatial validation data found MaxEnt consistently provided better model performance with 'good' model predictions (AUC =>0.8).
To arrive at an optimized architecture that best models the phenomenon, the model performance with different parameters is evaluated.
In Table 3, row "p" represented the model performance with complete predictor variables, while row "pi = 0" represented that with variable "pi" removed.
This would likely result in the same maximum SALI value for all data sets allowing more direct comparison s of model performance with the various data sets.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com