Sentence examples for classified accuracy from inspiring English sources

Accuracy is defined as the ratio of correctly classified examples to all examples classified: Accuracy = TP + TN TP + TN + FP + FN Where TP = true positive, TN = true negative, FP = false positive, and FN = false negative.

The performance of each classifier was assessed by two measures: (i) Percentage of samples correctly classified (Accuracy) and (ii) F measure, which is the harmonic mean of precision and recall.

Table 3 Performance of classifiers on unseen P2P botnets   Decision trees Random forests Bayesian network   Classified Classified Accuracy Classified Classified Accuracy Classified Classified Accuracy   malicious benign   malicious benign   malicious benign   Zeus 2,696 55 98 % 2,717 34 98.76 % 2,660 91 96.69Nugache 42 7 85.71% 43 6 87.76% 48 1 97.96%.

Analysis was carried out for the classification of patients with PCA and typical Alzheimer's disease for each oculomotor metric, choosing the cut-off that maximized the percentage of patients correctly classified (accuracy).

Extensive experimental results on real-world datasets have validated our approach in terms of classifying accuracy and computational efficiency.

Through various experiments on MNIST variation datasets, FCCNN achieves classifying accuracy comparable to the state-of-the-art methods while requires significantly reduced training time.

For the Twist 1 sample set, the standard deviation of each dimension is relatively small and the sensitivity of classifying accuracy on T is reduced; however, when the T is selected as 200, the maximum classification accuracy is achieved.

If we imagine a cardiac output monitor as a gun that is used to shoot a target (the cardiac output), we can classify accuracy as the characteristic of being able to shoot close to the centre of the bull's-eye.

Though many phenotypes (more than 55%) were classified with accuracy above 80%, we used a 60% classification accuracy cutoff to accommodate noise in input data such as wrong gene calling or imbalance in phenotype data.

The best classification accuracy obtained by gene pairs was 12 test samples classified correctly; accuracy was 63.16%.

Taking the sperms whose sum value is larger than 3 as normal and the sperms whose sum value is smaller than 4 as abnormal, we can tell that only 39 sperms are mistakenly classified, providing accuracy of 75.625%.