Decision Trees

Decision Trees

Decision Trees are powerful and provide an output that domain experts and practitioners can easily understand. Decision Trees provide the basis for many Ensemble Methods, which involve using multiple models for inference and producing the output for the datasets at hand.

Decision Tree, as the name suggests, is constructed in a Tree manner, including a root node, internal nodes, and leaf nodes. Leaf nodes, also known as terminal nodes, give us the class of the instances falling in that terminal node, and the goal is to have homogeneous terminal nodes. Root Node refers to all the instances in the dataset. Interior nodes partition the set of instances. Once created, a tree can be navigated with a new row of data following each branch with the splits until a final prediction is made.