In Decision Tree modeling, what is the measurement scale of selected inputs that allows each unique value to serve as a potential split point?

In Decision Tree modeling, the measurement scale of selected inputs that allows each unique value to serve as a potential split point is typically an interval scale. This is because interval data consists of numbers that represent ordered categories and the intervals between the numbers are meaningful, which allows for the identification of thresholds to make splits in the data.

For interval-scaled inputs, every unique value can be considered for splitting the data, enabling the decision tree algorithm to analyze where the best division occurs. This characteristic is essential for modeled decision boundaries within the tree, as it ensures that the algorithm can make decisions based on the magnitude of the differences between those values, leading to more precise classifications or predictions.

In contrast, while categorical and nominal inputs can also be used in decision trees, they function differently. Categorical data categorize inputs into distinct groups, and nominal data specifically refers to categories without any inherent order or ranking. Binary data is a subset of categorical data that only takes on two values. These measurement types do not provide the same granularity and potential for split points as interval data does in decision trees.