The difference between accuracy and recall
时间: 2023-11-02 17:07:05 浏览: 47
Accuracy and recall are both metrics used to measure the performance of a classification model.
Accuracy is the ratio of correctly predicted observations to the total number of observations. It measures the overall correctness of the model's predictions. A higher accuracy score indicates that the model has made more correct predictions.
Recall, on the other hand, is the ratio of the correctly predicted positive observations (true positives) to the total number of actual positive observations (true positives + false negatives). It measures the ability of the model to identify all positive examples. A higher recall score indicates that the model is better at identifying positive cases.
In summary, accuracy measures the overall performance of the model, while recall measures the model's ability to identify positive cases. They are both important metrics to consider depending on the specific application and goals of the model.