DTs are commonly considered a supervised learning technique used for solving classification and regression tasks.
More technically, each branch in a DT represents a possible decision, occurrence, or reaction, in terms of statistical probability. Compared to naive Bayes, DTs are a far more robust classification technique. The reason is that at first, the DT splits the features into training and test sets. Then, it produces a good generalization to infer the predicted labels or classes. Most interestingly, a DT algorithm can handle both binary and multiclass classification problems.
For instance, in the following example figure, DTs learn from the admission data to approximate a sine curve with a set of if...else decision ...
