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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.
Our preliminary results show a good model performance with R2 = 0.81.
References. 1. Reuschenbach P et al. ECOSAR model performance with a large test set of industrial chemicals.
To arrive at an optimized architecture that best models the phenomenon, the model performance with different parameters is evaluated.
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Karlson et al. [37] reported model performances with an R2 of 0.57 and RMSE of 17.6 t/ha (relRMSE 66%) when mapping aboveground biomass in Sudano-Sahelian woodlands using multi-temporal (7 observations) Landsat OLI products.
In the microarray data experiments we show that careful choice of M produces stable model performances with a comparable CCR range than the nearest SVM competitor.
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.
Model performance increased with the removal of proteins associated with few diseases, which affected only the positive class in the prediction.
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Justyna Jupowicz-Kozak
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