Sentence examples for percentage of examples from inspiring English sources

Each row represented one rotated vowel (or consonant), and each column represented a natural vowel (or consonant) category, so that each cell represented the percentage of examples of a particular rotated vowel (or consonant) identified within a natural phonological category.

Accuracy, the most common and perhaps also the most intuitive metric to evaluate the performance of a predictive model, measures the percentage of examples that are predicted correctly.

The percentage distribution of examples of the weA type according to the process concerned is given in Table 6.

Give a percentage of negative examples, such as 10percentt, it can draw an initial decision boundary to cover most of the positive and unlabeled examples.

end{aligned}Sensitivity (true positive rate)—the percentage of positive examples predicted correctly: begin{aligned} hbox {Sensitivity}=frac{mathrm{TP}}{mathrm{TP}+mathrm{FN}}.

For the phoneme classification task, the classification error is used, which is the percentage of misclassified examples in the training or testing data set.

For our data, the average percentage of positive examples was close to 50: 41, 57 and 63 %, for each of the three EC50 cutoffs respectively, so this is not a major concern.

end{aligned}Specificity (true negative rate)—the percentage of negative examples predicted correctly: begin{aligned} hbox {Specificity}=frac{mathrm{TN}}{mathrm{TN}+mathrm{FP}}, end{aligned}where TP is true positive, TN is true negative, FP is false positive and FN is false negative.

3. The percentage of training examples is around 24% (30 divided by 127).

The final number of generated reads in all HMP, GOS and GNHM subsets together with the corresponding percentage of positive examples is shown in Table 1.

The trade-off for this higher PPV is a decrease in the sensitivity, i.e., the percentage of positive examples that are correctly identified.