Supervised learning requires the use of labelled data on which an algorithm is trained, and training sets are called labeled data because the value for the class label is known.
For instance, to calculate an AUC value for the class "susceptible", we consider both the "moderately resistant" and the "resistant" as a new "non-susceptible" class.
When the classified PAD variable was used, severe PAD dominated the significant association of PAD with the risk of death; however, the overall P value for the class variable was not significant (P = 0.065).
In heuristic methods, field observation and expert's knowledge are used to identify landslides, make a prior assumption about past and future landslide movements on the site, assign weighted values for the classes of index maps and overlay them to produce a landslide susceptibility map.
It has been previously demonstrated that using discriminatory features, i.e. those features which assume statistically different values for the classes under consideration, enhances the performance of SVM-based classification [3].
Then, we determined mean FROH values for the classes defined in Table 2 for different allowed numbers of heterozygous calls.
The area values for the classes were transferred to an Excel worksheet, and the percentage of PAS or ORO content was calculated as [area pixels (PAS or ORO)/area pixels (PAS or ORO) + area tissue]*100.
Suitable areas for conversion to each class were assigned by giving the value for the five classes from 0 (no probability for conversion) to 1 (high probability for conversion).
Hence, convective requirement is mass independent throughout development, from ectothermy to endothermy, being similar to previously published values for the class Mammalia.
Table 6 summarises P-values for the class-by-class comparisons.
