- The [[Probability]] score is not reality, or ground truth.
- There are four possible outcomes for each output from a binary classifier.
- For the spam classifier example, if you lay out the ground truth as columns and the model’s prediction as rows, the following table, called a confusion matrix, is the result:
| Actual positive | Actual negative | |
|---|---|---|
| Predicted positive | True positive (TP): A spam email correctly classified as a spam email. These are the spam messages automatically sent to the spam folder. | False positive (FP): A not-spam email misclassified as spam. These are the legitimate emails that wind up in the spam folder. |
| Predicted negative | False negative (FN): A spam email misclassified as not-spam. These are spam emails that aren’t caught by the spam filter and make their way into the inbox. | True negative (TN): A not-spam email correctly classified as not-spam. These are the legitimate emails that are sent directly to the inbox. |