• 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.