Decision Tree
A Decision Tree is often used in classification problems for both categorical and numerical data. It works for both categorical and continuous input and output variables. It can be classified as supervised learning. The decision tree builds classification or regression models in a tree structure that dissects the dataset into smaller and smaller subsets, simultaneously building the association structure. The best-predicting, top decision node is called the root node.
Applications
Classify disease group and non-disease group as well as to distinguish among different disease sub-types, using features generated from protein sequence and structure data ref.
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