Precision is the fraction of values that actually belong to a positive class out of all the values which were predicted to belong to that class precision = true positives / (predicted true positives + predicted false positives)
Example:
There are 100 credit card transactions; 80 transactions are legitimate (positive class) and 20 transactions are fraudulent. If your model predicts that 85 transactions are legitimate, its precision is: 94.12% = 80 true positives / (80 true positives + 5 false positives)
