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Decision tree (simple).
The tested classifiers and their parameters are the following: 1. Decision tree (simple).
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The predictive modeling approaches for this research include artificial neural networks, decision trees, simple probabilistic classifiers, and ensembles.
Decision tree is simple to understand and to interpret since trees can be visualized.
The decision tree is simple to understand and to interpret by visualization.
On the other hand, the decision tree uses simple heuristics, which are easy to implement in a variety of hardware and software frameworks.
Figure 1 shows the decision tree for simple cataract.
Especially, decision trees are simple to understand and interpret.
In addition, the decision tree algorithm is simple to implement and can naturally capture interactions because each subsequent split is conditional on previous splits.
A decision tree is a simple tree structure whose non-terminal vertices represent tests on one or more attributes, while the terminal ones reflect the results of the decision.
We provide two decision tree algorithms using simple clinical and laboratory data that can be easily implemented in resource-limited countries to differentiate patients with influenza from those with dengue.
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