What type of predictions approximate the expected value of the target conditioned on the input values?

The concept of predictions that approximate the expected value of the target conditioned on the input values aligns directly with the notion of estimates in a statistical or modeling context. Estimates are calculated using the relationships identified in the data, providing a numerical approximation of the expected output (or target) for given input features.

When model training occurs, the algorithm learns from the training data to form a statistical relationship between input variables and the desired outcome. An estimate represents this relationship by outputting a predicted numerical value, which reflects the average or expected outcome conditioned on the specific input values. For example, if one were predicting house prices based on features such as size, location, and age, the estimate would yield an expected price for a specific combination of those features.

In contrast, decisions refer to the choice of actions based on input variables rather than a numerical approximation. Ranking involves ordering items based on predicted probabilities or scores, and forecasts typically pertain to time-series analysis where predictions are made about future values rather than providing estimates based on current inputs. Thus, the nature of the question specifically concerns the numerical approximation of an expected value, leading to the conclusion that estimates are the most appropriate choice.