Mean average precision (MAP) is a metric used to evaluate the performance of information retrieval systems, specifically in ranking and retrieving relevant documents. It considers both the precision of the system at various cut-off levels and the average precision across multiple queries, providing a holistic view of how well a model can retrieve relevant data while minimizing irrelevant results. This metric is particularly significant in applications involving graph neural networks, where understanding relationships and relevance among nodes is crucial for accurate predictions.
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