Which of the following is a method to assess the quality of splits made by a decision tree?

Entropy is a measure used to evaluate the impurity or disorder in a dataset and serves as a crucial metric in assessing the quality of splits made by a decision tree. When splitting the data into branches, a decision tree aims to maximize the information gained, which is effectively the decrease in entropy. By calculating the entropy for subsets of data resulting from a split, one can determine how well the split separates different classes. A lower entropy after a split indicates a higher quality split since it suggests that the resulting subsets are more homogeneous regarding the target variable.

Using entropy, the decision tree algorithm can decide which variable to split on and how to split it, promoting splits that lead to a purer outcome in terms of target class distribution. This characteristic makes entropy a fundamental concept in the context of decision tree construction and assessment. Other options, while potentially relevant in different contexts, do not specifically measure the effectiveness of decision tree splits in the same way.