Precision and recall
en.wikipedia.org · 4,109 words · saved by 2 readers
In classification, precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space.
Precision and recall In classification, precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances. Written as a formula: Recall (also known as sensitivity) is the fraction of relevant instances that were retrieved. Written as a formula: Both precision and recall are therefore based on relevance. Consider a computer program for recognizing dogs (the relevant element) in a digital photograph. Upon processing a…
saved by
related reading
- Sensitivity and specificity - Wikipediaen.wikipedia.org
- Precision-Recall Curve is more informative than ROC in imbalanced data | Towards Data Sciencetowardsdatascience.com
- Kullback–Leibler divergence - Wikipediaen.wikipedia.org
- Classificationfairmlbook.org
- Receiver operating characteristic - Wikipediaen.wikipedia.org
- Information Retrievaljzhao.xyz
- Understanding Micro, Macro, and Weighted Averages for Scikit-Learn metrics in multi-class classification with exampleiamirmasoud.com
- CoT monitorability: why g-means and not F1?substack.com
- terminology - F1/Dice-Score vs IoU - Cross Validatedstats.stackexchange.com
- Fundamentals of predictionmlstory.org
- Classification vs. Prediction – Statistical Thinkingfharrell.com
- Scoring rule - Wikipediaen.wikipedia.org