想起, also known as sensitivity or true positive rate, is a key performance metric used to evaluate the effectiveness of a classification model in the 人工知能の分野 and 機械学習. It quantifies the ability of a model to correctly identify all relevant instances within a dataset.
数学的には、リコールは真陽性の結果の数を実際の陽性インスタンスの総数で割った比率として定義されます。リコールの式は次のとおりです：
リコール = 真陽性 / (真陽性 + 偽陰性)
この式において：
- 真陽性（TP） 正しく陽性と分類されたインスタンスを指します。
- 偽陰性（FN） are the instances that are incorrectly classified as negative, despite being positive.
Recall is particularly important in scenarios where the cost of missing a positive instance is high, such as in medical diagnoses or 不正検出. A high recall score indicates that the model is effective at capturing most of the relevant instances, while a low score suggests that many positive instances are being overlooked.
However, it is essential to consider recall in conjunction with other metrics, such as precision (the ratio of true positives to the total predicted positives) and F1 score (the 調和平均 of precision and recall), to get a comprehensive understanding of a model’s performance. Balancing recall and precision is crucial, as focusing solely on maximizing recall may lead to a high number of false positives.