In this section, we will discuss the DT algorithm in detail. A comparative analysis of Naive Bayes and DT will be discussed too. DTs are commonly considered as a supervised learning technique used for solving classification and regression tasks. A DT is simply a decision support tool that uses a tree-like graph (or a model of decisions) and their possible consequences, including chance event outcomes, resource costs, and utility. More technically, each branch in a DT represents a possible decision, occurrence, or reaction in terms of statistical probability.
Compared to Naive Bayes, DT is a far more robust classification technique. The reason is that at first DT splits the features into training and test set. Then it produces a good generalization to infer the predicted labels or classes. Most interestingly, DT algorithm can handle both binary and multiclass...
