A decision tree is composed of several key components that define its structure and partition logic:

• Nodes: Split points where the dataset is partitioned based on feature values.
• Split point: The threshold or decision rule that separates one class or range from another.
• Pure node: A node where all observations belong to a single class, representing perfect classification.
• Mixed node: A node containing a mixture of classes that requires further splitting.
• Leaf nodes: The terminal nodes at the end of a branch that represent the final predicted class or value.
• Informativeness of split: A measure of the information gained from a split, used to select the optimal split point.