What does recall refer to in classification?(1 Point)The proportion of only the correct data pointsThe proportion of all data pointsBoth the aboveNone of these

Analysis of the Question

1. Understanding Recall in Classification: Recall, often referred to as sensitivity or true positive rate, is a key metric in classification problems. It indicates how well the model identifies positive instances within the dataset.

2. Possible Choices:

   • The proportion of only the correct data points: This suggests measuring performance based only on the correct predictions, which does not accurately define recall.
   • The proportion of all data points: This phrase is vague and misleading as it suggests a broader measure that does not align with the specific focus of recall.
   • Both the above: This option is also incorrect, as neither of the individual statements accurately describes recall.
   • None of these: This option remains as potentially correct if none of the previous statements accurately represent recall.

Final Answer

The correct interpretation of recall in classification does not align with any of the provided statements. Recall is defined as the ratio of true positives to the sum of true positives and false negatives, effectively measuring how well the model identifies positive instances. Therefore, the most accurate choice is:

None of these.