Recordar, also known as sensitivity or true positive rate, is a key performance metric used to evaluate the effectiveness of a classification model in the campo de la inteligencia artificial and aprendizaje automático. It quantifies the ability of a model to correctly identify all relevant instances within a dataset.
Matemáticamente, la recuperación se define como la proporción de resultados verdaderamente positivos en relación con el número total de instancias positivas reales. La fórmula para la recuperación es:
Recuperación = Verdaderos Positivos / (Verdaderos Positivos + Falsos Negativos)
En esta fórmula:
- Verdaderos Positivos (TP) se refieren a las instancias que son clasificadas correctamente como positivas.
- Falsos Negativos (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 detección de fraudes. 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 media armónica 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.