Precision is a metric used to evaluate the performance of a binary classification model. It is the ratio of true positives (TP) to the total number of positive predictions (TP + FP).
In other words, precision measures the accuracy of positive predictions made by the model. It answers the question, “Out of all the positive predictions made by the model, how many were actually correct?”
The formula for precision is:
Precision = TP / (TP + FP)
A high precision score means that the model makes few false positive predictions and is good at identifying positive instances. However, a high precision score alone does not necessarily mean that the model is performing well. It could be that the model is missing a large number of positive instances, leading to low recall.
