Supervised learning classification trees

The decision tree classifier is chosen for its favorable tradeoff between performance and implementation simplicity. Classification using DT is a supervised learning technique, the input of the learning algorithm is a set of known data and the output is a tree model similar to the ones shown in Figure 5. Once the tree is defined, the classification of new inputs starts at the root decision node of the tree and terminates at one of the leaf nodes that represent a specific class, passing by intermediate decision nodes. 

Like most supervised learning methods, the goal of the decision tree methodology is to develop classification rules that determine the class of any object from the values of the object s attributes. In the case of decision trees, as the name implies, the classification rules are embodied in a knowledge representation fonnalism called a decision tree. This method has been used to derive structure-activity relationships and to learn classification rules for reactions. 

Classification describes the process of assigning an instance or property to one of several given classes. The classes are defined beforehand and this class assignment is used in the learning process, which is therefore supervised. Statistical methods and decision trees (cf. Section 9.3) are also widely used for classification tasks. 

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