Two evaluation metrics used to evaluate a machine learning model’s performance are precision and recall. The percentage of all positive classifications that a model classifies as positive is called precision. Recall indicates how many of the real positive items the model was able to identify.According to experts, in order to reduce the number of false positives (FPs) in a model, precision evaluates how many positives the model properly predicted. The ratio of true positives (TPs) to the total number of positives in the data set is what this represents.A high precision model’s main advantage is its capacity to bias for accuracy when separating true positives against false positives.Recall evaluates a machine learning model’s ability to categorize every true positive in the dataset.While recall is the proportion of all real positive cases that users’ models correctly identify, precision measures the proportion of users’ projected positive cases that are true positives. Whereas recall is crucial when the cost of missing true positives is high, precision is important when the cost of false positives is high.While increasing recall often means sacrificing precision, increasing precision often means sacrificing recall.
