Micro-Precision Metric
(Redirected from micro-precision metric)
Jump to navigation
Jump to search
A Micro-Precision Metric is a precision metric that aggregates the contributions of all classes to compute the average precision across all classes in multi-class classification tasks.
- Context:
- It can (typically) be calculated by summing up the True Positives and False Positives.
- It can (typically) provide a more balanced measure of a model's accuracy in predicting positive instances across all classes, by treating every instance prediction equally, regardless of class.
- It can be particularly valuable in datasets where one or more classes dominate over others (as it prevents the model performance from being biased towards the dominant classes).
- ...
- Example(s):
- ...
- Counter-Example(s):
- Macro-Precision, where the calculation of precision is done for each class individually and then averaged, without considering the class imbalance.
- Micro-Recall Metric, ...
- Micro-F1 Score, ...
- See: Classification Accuracy, Macro-Recall, Micro-F1 Score, Model Evaluation Metrics
References
2005
- (Warf, 2005) ⇒ Benjamin C. Warf. (2005). “Comparison of 1-year Outcomes for the Chhabra and Codman-Hakim Micro Precision Shunt Systems in Uganda: A Prospective Study in 195 Children.” Journal of Neurosurgery: Pediatrics, 102(4).