Sentence examples for best predictive performance from inspiring English sources

As shown in Table 3, BindN-RF gives the best predictive performance with the prediction strength at 76.86% and ROC AUC equal to 0.8495.

Nevertheless, the classifier constructed using all the descriptors of evolutionary information (PSSM,, and ) appeared to give the best predictive performance with 75.2% prediction strength (71.6% sensitivity and 78.7% specificity), MCC = 0.393 and ROC AUC = 0.825 (Table 2).

Of the five biochemical features (H, K, M, P and Co), the hydrophobicity index (H) gave the best predictive performance at 74.70% prediction strength (71.62% sensitivity and 77.79% specificity), MCC = 0.4728 and AUC = 0.8237 (Table 2).

All models were shown to achieve outstanding predictive performance, the lowest being NS2 model with 96.57% accuracy (AUC = 0.980; MCC = 0.916), while HA prediction model achieved the best predictive performance of 98.62% accuracy (AUC = 0.998; MCC = 0.972).

ANN-raw, trained on uncompressed spectral data, shows best predictive performance (RMSEP < 2.25%) but longest computation (up to 10 min).

The best predictive performance was achieved by SPA-LS-SVM (Raw) model using 22 EWs, and the prediction results were Rp = 0.9962 and RMSEP = 0.0339 for the prediction set.

The best predictive performances of the models were assessed by means of various descriptive statistical indicators.

(A) Observed absolute errors for boosted training set A, which had the best external predictive performance (sPRED = 0.633; sCV = 0.762).

(B) Observed absolute errors for boosted training set B, which had the best internal predictive performance (sCV = 0.681; sPRED = 0.637).

As discussed below (see Experimental methods), the various performance statistics were computed as the value of t, the threshold similarity, was systematically varied, so as to determine the cut-off value, t ROC, that resulted in the best overall predictive performance.

Theoretically, and in the discussed simulations, minimum mean AIC is related to best mean predictive performance, where the mean is taken across multiple studies and prespecified models.