Mean Average Precision at K (MAP@K) clearly explained
One of the most popular evaluation metrics for recommender or ranking problems step by step explainedPhoto by Joshua Burdick on Unsplash.Mean Average Precision at K (MAP@K) is one of the most commonly used evaluation metrics for recommender systems and other ranking related classification tasks. Since this metric is a composition of different error metrics or layers, it may not be that easy to understand at first glance.This article explains MAP@K and its components step by step. At the end of this article you will also…