Today I will focus on the concept of Precision in data science, particularly in the context of classification models. In the document that I am sharing, outlines when to use precision (when false positives are costly or high confidence in positive predictions is needed) and when not to use it (when balanced performance or recall is required). It also explains how to interpret precision values and provides examples of its use in medical screening, email spam detection, and fraud detection.
Today in this document I will cover the following:
Table of Contents:
1. When False Positives Matter: Precision as a Key Metric in Data Science (Page 1)
2. Precision Using Confusion Matrix (Page 2)
3. When to Use Precision? (Page 3)
4. When Not to Use Precision? (Page 4)
5. What are Good Precision Values? (Page 5)
6. Use Cases (Page 6)
a. Medical Screening for Rare Diseases (Page 6)
b. Email Spam Detection (Page 7)
c. Fraud Detection (Page 8)
7. Summary (Page 9)
Hope you find this document helpful.
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