In a decision tree, what does each internal node represent?

In a decision tree, each internal node represents a feature or attribute of the data. This is a fundamental aspect of how decision trees operate. At each internal node, the data is split based on the values of the attribute in question, effectively partitioning the dataset into subsets that are more homogeneous with respect to the target variable.

The decision-making process continues at each internal node, leading to further splits, until it reaches the terminal nodes, or leaves, which provide the final predictions. This structure allows a decision tree to systematically narrow down to specific outcomes based on the characteristics of the input data.

The other choices depict aspects of decision trees but do not accurately define the role of internal nodes. The output variable is typically represented at the leaves of the tree, not the internal nodes. The final prediction also occurs at the leaves, as they contain the output of the decision process. Finally, a sample of training data refers to the dataset used to build the tree rather than the nodes themselves.