Precision Calculator
Accuracy of positive predictions.
Formula first
Overview
Precision, also known as positive predictive value, quantifies the accuracy of a model's positive classifications. It represents the proportion of items identified as positive that truly belong to the positive class.
Symbols
Variables
P = Precision, TP = True Positives, FP = False Positives
Apply it well
When To Use
When to use: Apply this metric when the cost of a false positive is high, such as in spam detection or legal judgments. It is most effective when you need to be confident that every positive result is legitimate, even if you miss some positives.
Why it matters: Precision is vital for maintaining system credibility and avoiding unnecessary actions triggered by false alarms. In fields like facial recognition or credit card fraud, high precision prevents inconveniencing innocent users or wasting investigative resources.
Avoid these traps
Common Mistakes
- Confusing precision with recall.
- Using FN instead of FP.
One free problem
Practice Problem
A malware detection system flags 100 files as malicious. Upon review, 85 were found to be actual viruses, while 15 were safe system files. Calculate the precision of the detection system.
Solve for:
Hint: Divide the count of true positives by the total number of positive predictions.
The full worked solution stays in the interactive walkthrough.
References
Sources
- Wikipedia: Precision and recall
- An Introduction to Statistical Learning: with Applications in R by James, Witten, Hastie, Tibshirani
- Wikipedia: Confusion matrix
- The Elements of Statistical Learning (Hastie, Tibshirani, Friedman)
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.).
- Wikipedia: Precision and recall (article title)
- OCR A-Level Computer Science — Algorithms and Data