Confusion Matrix | Precision Recall | Concepts Of Machine Learning | Best Explanation
A confusion matrix, also called a contingency table or error matrix, is used to visualize the performance of a classifier.
The columns of the matrix represent the instances of the predicted classes and the rows represent the instances of the actual class. (Note: It can be the other way around as well.)
In this confusion matrix, the "correct" cells are:
• TN: the number of true negatives, i.e., patients who did not have cancer whom we correctly diagnosed as not having cancer.
• TP: the number of true positives, i.e., patients who did have cancer whom we correctly diagnosed as having cancer
And the "error" cells are:
• FN: the number of false negatives, i.e., patients who did have cancer whom we incorrectly diagnosed as not having cancer
• FP: the number of false positives, i.e., patients who did not have cancer whom we incorrectly diagnosed as having cancer
Note:
Recall tells you how much of the +ve's you can find.
Precision tells you how much junk there is in your predicted +ve's.
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