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However, after building the classification tree, new metrics could be included in the tree in order to identify a larger number of problems, such as those related to mobility issues.
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A total of 60 2D and 3D molecular descriptors (MDs) of diverse nature were selected for building the classification models using decision tree (DT), random forest (RF), support vector machine (SVM), and moving average analysis (MAA).
Table 18 Classification quality of the classification tree method Pred.
To estimate the computational cost, let C tree be the cost of executing the classification tree.
Table 19 Classification quality of the classification tree method after merging Pred.
Goodness-of-fit of the obtained classification tree was compared to the cluster assignment of Multimix, by dropping individuals down the classification tree, and by comparing the R tree-based classification of subjects to the Multimix-based one.
2. Classification tree The classification tree generated using all four classification variables is shown in Figure 2.
Figure 2 shows the result of the classification tree analysis.
The goal was building a classification tree allowing allocation of individuals to 6 mutually exclusive classes of general needs for assistance, ranging from pure monitoring to extensive redesigning of facilitators and removing barriers.
Algorithm ?? describes the basic process of building a classification tree.
Rather than building a new classification tree, we decided to keep the significant variables of the different components of cost (medical, nursing, rehabilitation, logistic) in an additive way.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com