What is Precision?
Precision is a performance metric used in classification tasks to evaluate the accuracy of positive predictions made by a model. It is the ratio of true positive predictions (correctly identified positive instances) to the total number of positive predictions made by the model, which includes both true positives and false positives (instances incorrectly identified as positive).
Precision is defined as:
Precision = True Positives / (True Positives + False Positives)
Why is Precision important?
Precision is important in situations where the cost of false positives is high. For example, in a medical diagnosis setting, a high precision indicates that when the model predicts a patient has a certain disease, it is likely to be correct. A low precision, on the other hand, would mean that the model often misclassifies healthy patients as having the disease, which could lead to unnecessary treatments and increased healthcare costs.
Resources on Precision in Machine Learning
To learn more about precision in machine learning, you can explore the following resources: