What does "feature values" refer to in a decision tree?

Feature values in a decision tree refer to the measurable properties or characteristics of the data. In the context of decision trees, features are the attributes or variables that are used as inputs to make decisions about how to split the data at each node in the tree. Each feature can have various values, which represent specific measurements or categories related to the data points in the dataset.

For example, in a dataset related to housing prices, features might include the number of bedrooms, square footage, and the age of the house. Each of these features has corresponding values (e.g., a house might have 3 bedrooms, 1500 square feet, and be 10 years old), which help the decision tree determine the best splits to make accurate predictions.

The other options do not accurately describe feature values. Unique identifiers pertain to distinguishing records instead of their measurable characteristics. Errors in predictions relate to the performance of the model rather than the input data itself. Lastly, dependent variables, or outputs, are the results being predicted rather than the features used to make those predictions. Therefore, the correct answer highlights the essence of features in decision tree algorithms and their role in data analysis.