 Hybrid optimization-satisficing techniques are used in machine learning when many metrics are available but no sound or practical method is available for combining them into a single value. Instead, hybrid optimization-satisficing methods select one metric to optimize and satisfice the remainder. For example, a machine learning classifier might optimize accuracy (i.e., maximize the proportion of examples for which the model yields the correct output; see  section 8.2)  but set aspiration levels for the false positive rate, coverage, and runtime.