Specifically, we recommend that partitioning choices be carefully based on external considerations, such as gene duplication theory, taxonomic sampling, or phylogenetic patterns of niche variation.
Several discussions have raised legitimate arguments against the naturalness of data partitioning and choice of model selection [45] [48].
Formally, we partition the genome into haplotype blocks by any block-partition method of choice, and evaluate haplotypes in each block using training data from each of the source populations.
Considering this evidence for unreliability in the more highly partitioned model runs, we tempered our choice of partitioning scheme.
Moreover, decision trees themselves can be implemented using different variable selection methods, although recursive partitioning is the standard choice.
However, this approach requires an arbitrary choice of partition probability threshold to partition the sample.
Figure 5 depicts our choice for partitioning the 60 bits in the first K packets of each flow.
Since our bitmap-based hybrid BFS employs two-dimensional (R times C) partitioning, there is a choice of how to map this onto the six-dimensional Tofu network, whose dimensions are named "x, y, z, a, b, c".
In order to qualify the effectiveness of graph partitioning, it is natural choice to use two major metrics called replication factor [2] and load balance factor [3]. Replication factor [2] is a metric that evaluates communication cost among distributed computers.
This finding (fig. 1) contrasts with Pacheco et al. (2011) who found a monophyletic relationship of diurnal raptors, which could be due to the choice of partitioning scheme (see Powell et al. 2013).
