Final answer:
The right node in a decision tree is chosen based on the attribute with the highest information gain, as it most effectively reduces uncertainty in classification.
Step-by-step explanation:
The correct option is D:
When constructing a decision tree, the correct node is chosen by selecting the attribute with the highest information gain. While entropy is a measure of disorder within the system, higher entropy does not directly equate to being a better choice for a decision node. The information gain measures how well an attribute separates the training examples according to their target classification. In other words, it reflects how much uncertainty in the output is reduced after splitting on that attribute. Therefore, the goal is to reduce the entropy in the classification as much as possible, and the attribute that accomplishes this most effectively is one with the highest information gain.
Information gain is calculated by comparing the entropy of the parent node with the weighted average of the entropies of the child nodes. The attribute that results in the highest information gain is chosen as the splitting criterion for the right node.