The decision tree considers geological and geophysical factors affecting stress magnitudes both on the regional and local scale.
Based on different operating conditions, the Characteristic ELLipsoid (CELL) is used to offline train a decision tree considering the sources respectively added to different generators or loads.
In Step 6, the decision tree considers if there are data on the components that suggest an interaction other than dose additivity (i.e. antagonism, potentiation, or synergy).
Based on the presence/absence of each FISH-detected genomic aberration (Table 1), FNA biopsies were initially assigned a renal cortical neoplasm subtype according to a preliminary decision tree considering gains and losses of 10 chromosomes and one chromosomal rearrangement based on published cytogenetic studies 16, 20, 23, 27, 37, 42, 43.
For building all the decision trees, the time complexity is O(t · m · n · log(n)), where t is the number of decision trees considered in the model.
The classification result is weighted average of all of the terminal nodes that are reached, providing the final decision: For testing, the time complexity is O(t · n · m), where n is the number of feature vectors in the testing set, m is the number of features in every feature vector, and t is the number of decision trees considered in the model.
Mobility parameters for building the decision tree are considered as follows: Long distance, whenever the distance between two nodes is higher or equal to 200 m.
The features in a decision tree are considered as one feature subset, and each feature is a system component.
In constructing this decision tree, we considered pruning back to the first branch, at which point the NPV was 72%.
This model took the form of a decision tree and considered the full treatment pathway from the possible onset of postnatal depression through identification, treatment, and possible relapse (figure).
