Sentence examples for proportion of classifications from inspiring English sources

We computed the proportion of classifications in the MR that received concordant classifications by TMA and computed Kappa statistics for each of the four ER/PR subtypes (similar to the analysis in the GEM dataset).

The relationship between the SI index and the proportion of classifications of the Eff stimulus across the population of cells (Fig. 3A) shows a robust correlation between the SI of individual neurons and the classification of the AS image (r = 0.45, P = 0.0001, n = 67).

Although not reported here, in our case, the two measures to estimate the expected proportion of classification errors and the closeness of association between the latent variable and the joint variables shows that no big uncertainty would appear during the process of assignment.

The percentages of classification errors obtained by normalization methods could be used to obtain a 95% confidence interval of the mean of the proportion of classification errors for the normalization.

In evaluating binary classification accuracy, a simple proportion of correct classifications is often inappropriate.

Classification and permutation testing were performed separately for the left and right hemisphere and separately for Experiment 1 and Experiment 2. In order to investigate the consistency of correct classifications for each voxel, we calculated the proportion of correct classifications for each voxel across subjects.

Namely, the first principal component provides a nearly perfect classification of patients and controls with 0.98 AUC and 0.93 proportion of correct classifications (Figure 1).

Accuracy was used as measurement for the proportion of true classifications by DeconSeq and calculated for thresholds of 95% query coverage and varying alignment identity.

Unlike the original study [8] that used proportion of correct classifications as the performance metric, we employed area under the ROC curve (AUC) that has more power to detect predictive signal of SNPs [23] [25].

Decoding performance for each cross-validation was estimated as the proportion of correct classifications, and the final decoding accuracy was calculated by averaging performances from all twelve cross-validations.

Sensitivity (se) was defined as the probability of a true positive, se = tp/ tp+fn); specificity (sp) as of a true negative, sp = tn/(tn+fp); and accuracy (ac) as the proportion of correct classifications, ac = (tp+tn)/ tp+fp+tn+fn).