AI-900: What is Recall and How to Calculate It for Classification Problems

Learn what recall is, how to calculate it, and why it is important for classification problems. Recall is the fraction of positive cases correctly identified by a classifier.

Question

Which metric presents the fraction of positives cases correctly identified?

A. F1 Score
B. Recall
C. Accuracy
D. Precision

Answer

B. Recall

Explanation

The correct answer is B. Recall.

Recall is a metric that measures the fraction of positive cases correctly identified by a classifier. It is also known as sensitivity or true positive rate (TPR). Recall is calculated as the ratio of true positives (TP) to the total number of actual positives (TP + FN), where FN is the number of false negatives. Recall can be interpreted as the probability that a positive case is correctly classified by the classifier.

Recall is important when we want to minimize the number of false negatives, or cases that are missed by the classifier. For example, in a medical diagnosis scenario, we might want to have a high recall to ensure that we do not miss any patients who have a disease. However, recall does not take into account the number of false positives, or cases that are incorrectly classified as positive. Therefore, recall alone is not enough to evaluate the performance of a classifier. We also need to consider precision, which is the fraction of positive predictions that are correct, or the ratio of TP to the total number of predicted positives (TP + FP), where FP is the number of false positives. Precision can be interpreted as the probability that a positive prediction is correct.

A common way to combine recall and precision is to use the F1 score, which is the harmonic mean of recall and precision. The F1 score is calculated as 2 * (recall * precision) / (recall + precision). The F1 score is a balanced measure that considers both recall and precision. It is useful when we want to compare classifiers that have different trade-offs between recall and precision. However, the F1 score does not take into account the accuracy, which is the fraction of all predictions that are correct, or the ratio of TP + TN to the total number of cases (TP + TN + FP + FN), where TN is the number of true negatives. Accuracy can be interpreted as the overall correctness of the classifier.

To summarize, recall is a metric that presents the fraction of positive cases correctly identified by a classifier. It is useful when we want to minimize false negatives, but it does not consider false positives, precision, or accuracy. Therefore, recall should be used in conjunction with other metrics to evaluate the performance of a classifier.

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