2.1.3. Decision Tree (DT)

The decision tree algorithm is a popular supervised machine learning algorithm used for classification and regression tasks. It works by constructing a tree-like model of decisions and their possible consequences based on the data [46]. The decision tree algorithm works by dividing the feature space of the training set recursively. Its goal is to identify a collection of decision rules that can partition the feature space in a way that produces a reliable and informative hierarchical classification model. In this algorithm, each node represents an attribute or feature, and each branch represents an outcome. The root node represents the entire dataset, and at each internal node, the algorithm divides the data based on a specific attribute's value. The schematic of the DT algorithm is shown in Figure 1C. This process is repeated recursively until a stopping condition is met, such as achieving a specified level of purity or reaching a predetermined depth [46].
