Sentence examples for negative correctly from inspiring English sources

where Sens is Sensitivity, Spec is Specificity, tp stands for true positive rate (e.g. the number of correctly predicted active compounds), tn – for true negative (correctly predicted inactive compounds), fp – for false positive (inactive compounds that've been predicted to be active), fn – for false negative (active compounds that've been identified as inactive ones).

They are defined in Eq. (2) and Eq. (3), where TP and TN denote the number of positive and negative correctly classified cases, and FP and FN denote the positive and negative misclassified cases.

EPDS's accuracy (proportion of results, both positive and negative, correctly identified by the EPDS) was estimated by the area under the ROC curve.

Purchase a charge controller and connect the panel to the controller, making sure to connect the positive and negative correctly.

The DETECTER and SIFT approaches generate comparable predictions regarding the tolerability of phenotypic missense mutations in the CFTR protein, i.e., differentiating true negative (correctly-predicted tolerated) from false-negative (incorrectly-predicted tolerated) amino acid replacements.

For any method while it is very important to correctly recognize the positives, it is equally important (sometimes even more important) to recognize the negatives correctly.

Sensitivity is a measure of actual positives correctly identified and the specificity measures the proportion of negatives correctly identified: The false discovery rate (FDR) is the proportion of all predictions that are false, estimated from gold-standard negative and positive training sets (e.g. GSPmito or GSN∼mito).

TN True negatives, correctly predicted neutral mutations.

Our blinded behavioural analysis test returned 110 true negatives correctly identified as such, and 17 false positives (a pause in behaviour not associated with an EEE).

The true specificity for each test is the number of true negatives correctly identified as negative by that screening test divided by the total number of true non-cases.

Each table gives a paired estimate of sensitivity (the number of true positives correctly identified as positive by the test divided by the total number of cases) and specificity (the number of true negatives correctly identified as negative by the test divided by the total number of non-cases).