Sentence examples for classes precision from inspiring English sources

For small classes, precision, recall and are better measures.

For samples that did not include all age classes, precision increased with the number of classes sampled in scenarios A and C (Figs S4 and S5), but not for sturgeon (Fig. 5) and mussel (Fig. S6) life histories.

Figure 4 shows its achieved in-class precision and recall values along with the corresponding (F_{1}) values.

Since the model does not discriminate between entity types, there is no way to calculate per-class precision values and, by extension, F-scores.

Table 9 Performance of the SimpleLogistic algorithm for Android by class Class Precision Recall F-measure How-to-do-it 0.824 0.821 0.822 Debug-corrective 0.804 0.864 0.833 Others 0.733 0.625 0.675 Table 10 Performance of the Logistic algorithm for Swing by class Class Precision Recall F-measure How-to-do-it 0.788 0.795 0.791 Debug-corrective 0.832 0.885 0.858 Others 0.695 0.425 0.527.

Fig. 5 The tree generated by W-J48 Table 6 Confusion matrix for the W-J48 classifier   True satisfied True violated Class precision Predicted satisfied 225 66 77.32 Predicted violated 20 30 60.00 Class recall 91.84 31.25 –.

Fig. 4 The rules generated by W-PART Table 5 Confusion matrix for the W-PART classifier   True satisfied True violated Class precision Predicted satisfied 237 75.96.96 Predicted violated 8 21 72.41 Class recall 96.73 21.88 –.

Nevertheless, following Krallinger et al. [30] we report per-class precision in Table 2. It's important to note that following the CHEMDNER CEM evaluation rules we have only considered perfect matches.

These results are presented in Table 2. Table 2 Performance of classifiers on test data   Decision trees Random forests Bayesian network Class Precision Recall FP rate Precision Recall FP rate Precision Recall FP rate Malicious 95.3%93.4%0.2%95.3%94.9%0.2%91.9%88.4%0.3%.4% 0.3% Benign 99.7%99.8%6.6%99.8%99.8%5.1%99.5%99.7%11.6%7% 11.6%.

The model performance was compared by evaluating class accuracy, class precision, and kappa scores.

Class precision refers to the proportion of the given predicted class that were correctly classified.