Sentence examples for but the classification accuracy from inspiring English sources

As expected, the algorithm predicted class membership with decreasing accuracy as the ICC increased, but the classification accuracy of SLA forests was uniformly equal to or less than that of SLB and URF forests (except for a single case for 30 subjects with 2 replicates and ICC = 0.5).

On top of the 83 known, about 52 new PML nuclear proteins are predicted but the classification accuracy is poor.

The number of SNPs increased to 84 but the classification accuracy was reduced to 0.84 on average if Han Chinese and Japanese samples were further regarded as samples from different sub-Asian populations and classified with African and European samples jointly.

Overall, accuracy rates relying on all candidate features were relatively low (56 64 %) but the classification accuracy of MDD patients and HVs significantly increased after feature selection with GA and LDA, regardless of whether alpha total (85 86 %), low (88 89 %) or high alpha band (80 86 %) features were used in the modeling.

The estimated cut-point for the CRLF2 expression was ĉ = 1.46, the same for all three methods, but the classification accuracy of this biomarker was very low, as depicted by the ROC curve and expressed by the Youden index J = 0.10 calculated for the identified cut-point.

The classification accuracy reached as high as 0.87 for balanced accuracy, 0.95 precision, 0.85 recall, and 0.95 AUC value for all the training sets from data set 1, but the classification accuracy showed only around 5% improvement for training sets from data set 2. With polytomies being identified through the BLR classification, PolyPhy can make a multifurcating tree.

Preparation time was found to have a significant effect on performance and classification accuracy on a number of these tests, but incentive was found to have a significant effect on the performance but not the classification accuracy of one test (viz., the Multi-Digit Memory Test).

By using the selected features, not only the number of features is reduced, but also the classification accuracy is increased to 100% for all the three mentioned artificial neural networks.

We believe that a forward scheme is better because it first adds the highest discriminating features followed by features that individually may not be discriminating, but improve the classification accuracy when used in combination with the discriminating features.

This method provides comparatively satisfactory classification performance, but the classification accuracies are not consistent for all cases.

Higher dimension is better in the understanding of different states, but the classification accuracies of application can be lower as obtained in Ref [7].