The fault classifications are displayed through the information that got from the first and the second principal component and kernel principal component, and these demonstrate they have a different and good effect of classification.
Numbers of genes and GO classifications are displayed in Figure 8.
All the resulting classifications were displayed in Additional file 5 (Table S3) and used for the subsequent analysis.
After the calculation, commonly used statistical indicators related to classification are displayed, including number of positive samples, number of negative samples, AUC score, accuracy, MCC, F1 score, sensitivity, specificity and ROC curve.
The top 10 protein markers displaying the highest RI values for predictive classification are displayed in Figure 2A.
The relationship of specific spa types to PFGE nomenclature and IPC classification are displayed in Fig. 3 with additional data provided in the Appendix.
The pattern of land surface units from each attempt at classification is displayed immediately and can then be processed in the GIS alongside additional data that can assist with a visual assessment and comparison of a series of results.
This modified classification is displayed in Table 1.
(d) Stacked-bar/box-whisker plot: genotypic distributions (AA call – blue color, AB call – green color, and BB call – brown color) of SNPs and expression distributions (pink color) of genes selected in the classification model are displayed.
The principal diffusion directions of both tensors are displayed.
