  Classically, testing whether decision makers belong to specific preference
classes involves two main approaches. The first, known as the functional
approach, assumes access to an entire demand function. The second, the revealed
preference approach, constructs inequalities to test finite demand data. This
paper bridges these methods by using the functional approach to test finite
data through preference learnability results. We develop a computationally
efficient algorithm that generates tests for choice data based on functional
characterizations of preference families. We provide these restrictions for
various applications, including homothetic and weakly separable preferences,
where the latter's revealed preference characterization is provably NP-Hard. We
also address choice under uncertainty, offering tests for betweenness
preferences. Lastly, we perform a simulation exercise demonstrating that our
tests are effective in finite samples and accurately reject demands not
belonging to a specified class.
