Recall

Recall

Recall is a metric for a classifier or predictor's sensitivity in machine learning (ML). It is calculated as the proportion of the total number of real positive cases to the number of true positive predictions made by the classifier. In other words, it is the percentage of genuine positive examples that the classifier properly detected.

Recall is a crucial parameter in machine learning since it assesses how well the classifier can recognise successful cases. It is frequently used with another metric known as precision, which is defined as the proportion of true positive predictions to all of the classifier's positive predictions.

Precision and recall can trade off with an increase in recall potentially causing a fall in precision and vice versa. The threshold parameter, which establishes the minimal probability necessary for a prediction to be deemed accurate, can be used to manage this trade-off.

Recall, which is a general indicator of a classifier's or predictor's sensitivity in machine learning, is calculated as the ratio of true positive predictions to all real-world positive cases. It is a significant parameter that is frequently combined with precision to assess the effectiveness of a classifier.

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What is recall in machine learning?

Recall is a crucial parameter in machine learning since it assesses how well the classifier can recognise successful cases. It is frequently used with another metric known as precision, which is defined as the proportion of true positive predictions to all of the classifier's positive predictions.

Precision and recall can trade off with an increase in recall potentially causing a fall in precision and vice versa. The threshold parameter, which establishes the minimal probability necessary for a prediction to be deemed accurate, can be used to manage this trade-off.

Recall, which is a general indicator of a classifier's or predictor's sensitivity in machine learning, is calculated as the ratio of true positive predictions to all real-world positive cases. It is a significant parameter that is frequently combined with precision to assess the effectiveness of a classifier.