Also known as: Top k Accuracy
Top-k accuracy — это метрика оценки, измеряющая, как часто целевая метка входит в первые k предсказаний или ранжированных результатов. Она полезна для классификации, рекомендаций и систем на основе softmax, где несколько правдоподобных выходов могут считаться успешными.
The percentage of times that a "target label" appears within the first k positions of generated lists. The lists could be personalized recommendations or a list of items ordered by softmax. Top-k accuracy is also known as accuracy at k. [!NOTE] Note: The target label could be any class (not necessarily the ground truth class), so top-k accuracy is not always equivalent to traditional accuracy. Consider a machine learning system that uses softmax to identify tree probabilities based on a picture of tree leaves. The following table shows output lists generated from five input tree pictures. Each row contains a target label and the five most likely trees. For example, when the target label was maple, the machine learning model identified elm as the most likely tree, oak as the second most likely tree, and so on. 1 3 5 | ---------| elm maple poplar | oak poplar maple | oak locust linden | maple oak poplar | locust oak paw-paw | The target label appears in the first position only once, so the top-1 accuracy is: The target label appears in one of the top three positions four times, so the top-3 accuracy is: