Sentence examples for discriminant accuracy from inspiring English sources

Thus, on the whole, the discriminant accuracy will not be much improved even if the sampling is repeated.

Figure 2(B) shows the application of a decision line to the PC scores of the dermal measurements, which yield a higher discriminant accuracy of 18/18 tumor-associated raft spectra and 16/18 naïve raft spectra, for a sensitivity and specificity of 100%and89%9%, respectively, and an overall classification accuracy of 94% (34/36).

The AC signals were processed by discriminant analysis and accuracy of 96.4% is reported.

Surprisingly, we found that our final models constituting only chromatin marks (H3K4me1, H3K27me3 and accessibility data) predicted p63 binding with high accuracy (Discriminant Model - 89.8%(±0.3) sensitivity, Regression model – 0.083 ±0.001) MSE).

6MWT: Six-minute Walk test; FEV1: Percentage predicted Forced Expiratory Volume in 1 second; NCC: Number of Chronic Conditions; Transplant Status: pre- or post-transplant * Non-significant correlations Bold: test of convergent validity; unbold: test for discriminant validity The accuracy of the a priori hypotheses is reported in Table 4.

The discriminant model has an accuracy of 90.11% in the training set, with a high Matthews' correlation coefficient (MCC = 0.80).

That counties surrounding the epicenter-counties (not those at the epicenter) had the highest resilience values was largely a result of preevent conditions, and the degree of usefulness of the preevent conditions to inform resiliency is reflected by the discriminant analysis's classification accuracy.

However, the most parsimonious discriminant model achieved maximal accuracy with the addition of sBP/LVESVI response as a 2nd factor in the model.

All patients had a significant metabolic response to NAC, and pre- and post-treatment spectra could be discriminated with 87.9%/68.9% classification accuracy by paired/unpaired partial least squares discriminant analysis (PLS-DA) (p < 0.001).

Predicting the dominant species based on a linear discriminant analysis had an overall accuracy of only 76 % at best, which degraded the accuracies of the predicted volumes.

The predictive accuracy of the discriminant functions was slightly improved by optimising the thresholds between sample depth classes.