Sentence examples for accuracy classifying from inspiring English sources

We achieved 100% of accuracy classifying the blobs from the annotated subset (second and third column of Table 1) as either abandoned or removed.

Comparing the recognition rates observed for one-to-one composer classification tasks, one can see the robustness of our proposed method: the lowest reported accuracy is at 86.1% for a Chopin vs. Beethoven task compared with a 63.8% reported in [20], while the best performance marked a 99.5% accuracy classifying Vivaldi vs. Joplin.

We demonstrate that the transcriptional response to food intake is robust by constructing a classifier from the gene expression traits with >90% accuracy classifying individuals as being in the fasted or fed state.

We further identified the most significant DMRs (n = 57) that could, with 89% accuracy, classify the malignant samples, representing potential OC DNA methylation biomarkers.

MethPed could, with a high accuracy, classify many of these tumors as GBMs, ependymomas, or one of the medulloblastoma subgroups, demonstrating the benefit of using the MethPed classifier for identifying more likely diagnoses (Table  2).> Stratification of patients with pediatric tumors with differing biological behavior or responsiveness to specific therapies is urgently needed.

The Bayesian Network provides 95% accuracy in classifying the Apnea, 85% accuracy in classifying the HE, 60% accuracy in classifying the HY, and 100% accuracy in classifying the WNU.

The Bayesian Network has 77.8% accuracy for classifying the AF, 88.4% accuracy for classifying the CHF, 85.0% accuracy for classifying the HE, 65.0% accuracy for classifying the HY, and 100% accuracy for classifying the WNU.

A recent study has shown that the self-reporting race has an overall 87.2% accuracy in classifying all races and 72% accuracy particular in Hispanics.

As shown in Figure 5D, the highest accuracy was achieved at values of 0.6 and 0.7 (100% accuracy in classifying HIV-1 stages with DEPs and 82.5% accuracy with DEGs).

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%.

Using a simple Gaussian-likelihood classifier, we examine the accuracy in classifying the individual symptom sub-topic ratings by designing a multi-class classifier where each rating level is set as its own class.