We have a prediction model to predict if an object is a cat or not.

We tested it on 20 objects. Among those objects, some of them are cat, some are not. Here is the result:

	Actually true	Actually false
Predicted to be true	7	3
Predicted to be false	2	8

What is True Positive (TP), False Positive (FP), True Negative (TN), False Negative (FN)?

TP =

FP =

TN =

FN =

What are the precision and recall?

Precision = TP / (TP + FP) =

Recall = TP / (TP + FN) =

What are the sensitivity and specificity?

Sensitivity (True Positive Rate) = TP / (TP + FN) =

Specificity (True Negative Rate) = TN / (TN + FP) =