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Supervised Learning: Difference between revisions
→Precision and Recall
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Recall is (# correct) / (# ground truth) or (true positive) / (true positive + false negative). | Recall is (# correct) / (# ground truth) or (true positive) / (true positive + false negative). | ||
Precision measures how good your model is at negatives. 1.0 precision means the model did misidentify any negatives but may have missed some positives. | Precision measures how good your model is at negatives. 1.0 precision means the model did misidentify any negatives but may have missed some positives. | ||
Recall measure how good your model is at identifying all the positive examples. 1.0 recall means your model identified all the positives. | Recall measure how good your model is at identifying all the positive examples. 1.0 recall means your model identified all the positives. | ||
Recall is also known as sensitivity. | |||
F1 = 2 * precision * recall / (precision + recall) | F1 = 2 * precision * recall / (precision + recall) | ||