metrics recall
时间: 2023-09-26 12:04:45 浏览: 34
Recall is a metric used in machine learning to evaluate the performance of a classification model. It measures the proportion of actual positive cases that were correctly identified by the model. Specifically, recall is defined as the ratio of true positives to the sum of true positives and false negatives:
Recall = True Positives / (True Positives + False Negatives)
A high recall value indicates that the model is good at identifying positive cases and has a low false negative rate. However, a high recall value may also indicate that the model has a high false positive rate, which means it may classify some negative cases as positive. Therefore, recall should be used in conjunction with other metrics such as precision, accuracy, and F1 score to get a more complete picture of the model's performance.