Precision at K: Evaluating Model Precision Beyond Binary Classification

Photo by ilgmyzin on Unsplash

Precision at K, also known as P@K, is a variation of the precision metric that becomes relevant in scenarios where the ranking or the top-K predictions of a model are essential. This concept is commonly applied in recommendation systems, information retrieval, and multi-label classification tasks where the goal is to identify the most relevant items or labels within a list of predictions.

Precision at K (P@K) Formula:

P@K = Number of Relevant Items in Top-K Predictions​ / K

Here, “relevant items” refer to the correct or true positive instances, and K is the number of predictions considered.

I. Scenarios Where Precision at K is Relevant:

1. Recommendation Systems:

• In recommendation systems, users are typically presented with a ranked list of items (movies, products, articles, etc.). Precision at K helps evaluate how well the system performs in terms of recommending items that the user finds relevant within the top-K recommendations.

2. Information Retrieval:

• In information retrieval tasks, such as search engine result pages, users are interested in finding relevant…