Final answer:
The final objective of a Decision Tree is to minimise the Gini impurity of the leaf nodes, ensuring that each node represents a single category for accurate classification, which aligns with the principle of maximum parsimony and aims for the greatest utility.
Step-by-step explanation:
The final objective of a Decision Tree is to minimise the Gini impurity of the leaf nodes. With this minimization, the tree produces nodes with a high homogeneity meaning that each node should ideally contain data that belongs to a single class.
This condition aligns with the principle of maximum parsimony, as seen in the creation of phylogenetic trees in biology, which seeks to simplify the explanation of events and patterns; however, in the context of decision trees used in data analysis, and particularly for classification tasks, the goal is for each leaf node to clearly represent one category or classification as opposed to a mix of different categories, which would indicate a high level of impurity.
By achieving low Gini impurity in leaf nodes, the Decision Tree ensures that the classification process is as clean and as accurate as possible, thus being able to produce the greatest utility in decision-making.