Average Precision

Average Precision (AP) is a metric commonly used in the context of binary classification and information retrieval to summarize the Precision-Recall curve. It provides a single number that characterizes the quality of the ranked retrieval results for a given class or category, especially in tasks like object detection.

How Average Precision Works

1. Computing AP:

1. Average Precision computes the weighted mean of precisions at each threshold:
2. $$AP = \Sigma(Recall_{n}-Recall_{n-1})*Precision_{n}$$
3. Where $n$ denotes a specific data point on the Precision-Recall curve.

2. Interpretation:

1. AP values range between 0 and 1, where a value of 1 means the model's predictions are perfect (though this is rare in practice).
2. AP considers both recall and precision in its computation, so it offers a balance between these two metrics.