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In summary, the AUC, Hosmer emeshow goodness-of-fit test, LR test, and well predicted outcome percentages were used to evaluate predictive accuracy.
Thus, 1871 human promoter sequences (from -3000 to +3000) in the EPD comprise the independent test set used to evaluate predictive performance.
Jackknife procedure was used to evaluate predictive capability of the models.
Calibration sets are used to evaluate predictive ability of the model and to accumulate a list of (alpha) (compound nonconformity) values.
The test data set was used to evaluate predictive performance.
Receiver operating characteristic area under the curve (AUROC) was used to evaluate predictive values.
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Visual predictive checks (VPCs) were used to evaluate the predictive performance of the bridged KPD model on both the adult data set and the paediatric data set.
The aROC curve was used to evaluate the predictive power of the various prediction models.
A similar set of proteins has been previously used to evaluate the predictive value of functionally important residue predictions [3].
Logistic regression (LR) and a naïve Bayes algorithm (NB) were used to assess statistically significant differences and the predictive power of hypertension, and a wrapper-based machine learning method was used to evaluate the predictive power of combinations of physical factors.
Various statistical measures were used to evaluate the predictive ability of the model, showing that it was suitable for predicting the carbon stocks of soils.
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