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Also, Sensitivity and Specificity features were comparable to the predictive model performance estimated on cell lines by the bootstrapping protocol.
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Random forest code was written in Java for this study, and all statistical analyses (e.g., model performance estimates) were performed using R.
The blade model performance was estimated in terms of rotation torque coefficient and thrust coefficient.
Images of the textured phantom and a corresponding uniform phantom were acquired at six dose levels and observer model performance was estimated for each condition (5 contrasts x 6 doses x 2 reconstructions x 2 backgrounds = 120 total conditions).
Model performance was estimated using leave-one-out cross-validation.
Risk model performance was estimated using a validated bootstrap bias-correction procedure.
Model performance was estimated with the Nagelkerke Pseudo- R2 statistic (a measure of explained variation in the model).
Predicted water and sediment concentrations of PAHs (SI Table S7) were used in the food web model to demonstrate overall model performance using estimated emission rates and physiochemical parameters without corrections or calibrations using water, sediment, or other measurements.
Model performance was estimated and compared as extra-sample via bootstrapping (100 replicates), considering out-of-bag distributions, and assessing significance via t-tests adjusted for sample overlap and multiple comparisons[ 39- 41].
As measures of overall model performance, we estimated the Brier score and Nagelkerke's R, which are measures of how well future outcomes are likely to be predicted by the model [ 30]; a higher Nagelkerke's R and a lower Brier score indicates better predictability of the model.
The framework specifies that a degradation model not only describe an aging process but also enable the adaptation of model-based performance estimates to online measurements of data pertaining to the aging process.
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