Decision tree is a popular classifier that does not require any knowledge or parameter setting. The approach is supervised learning. Given a training data, we can induce a decision tree. From a decision tree we can easily create rules about the data. Using decision tree, we can easily predict the classification of unseen records.
What Is a Decision Tree?
The following figure uses a decision tree to perform data classification.
Decision tree is a hierarchical tree structure that used to classify classes based on a series of questions (or rules) about the attributes of the class.
| The attributes of the classes can be any type of variables from binary, nominal, ordinal, and quantitative values, while the classes must be qualitative type (categorical or binary, or ordinal). In short, given a data of attributes together with its classes, a decision tree produces a sequence of rules (or series of questions) that can be used to recognize the class. |
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