The correct choice is Single-label multiclass classification. This approach fits because the problem requires assigning exactly one cuisine from a fixed set of four options to each dish.

Single-label multiclass classification trains a model to map each input to one and only one class when there are three or more possible classes. In practice you label each dish with a single cuisine and train with a categorical cross entropy loss and a softmax output so the model predicts one class per example.

Binary classification is not appropriate because it only models two possible outcomes and cannot directly handle a choice among four cuisines.

Amazon Comprehend is an AWS natural language processing service rather than a conceptual model type. It can perform text classification as a service but it is not the classification technique that defines single versus multi label setups.

Multi-label classification allows multiple labels per example and therefore contradicts the requirement that each dish receive exactly one cuisine.

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