Predictive Modeling
Machine Learning
The proportion of true positive predictions compared to all positive predictions made. Shows how many of the items identified as positive are actually positive.
Detailed Explanation
Precision is a metric in machine learning that measures the accuracy of positive predictions. It is calculated by dividing the number of true positive predictions by the total number of positive predictions (true positives plus false positives). High precision indicates that most items labeled as positive are genuinely positive, which is especially important in applications where false positives are costly.
Use Cases
- •Prioritizing accuracy in spam detection to minimize false positives and ensure genuine emails are correctly flagged.
Related Terms
Other terms in the Machine Learning category