# Confusion Matrix

A confusion matrix, typically represented as a table, is a popular evaluation metric used to describe the performance of a classification model (or "classifier"). The table compares predicted and actual values. The basic components of the table are as follows:

• True positives (TP): The prediction was yes, and the true value is yes

• True negatives (TN): The prediction was no, and the true value is no

• False positives (FP): The prediction was yes, but the true value was no

• False negatives (FN): The prediction was no, but the the true value is yes

The confusion matrix is closely related to other metrics like Precision, Recall/Sensitivity, Specificity, and F1 Score. Those definitions are as follows:

 Metric Formula Definition Accuracy (TP+TN)/(TP+TN+FP+FN) Percentage of total items classified correctly Precision TP/(TP+FP) How accurate the positive predictions are Recall/Sensitivity TP/(TP+FN) True positive rate (eg to asses false positive rate) Specificity TN/(TN+FP) True negative rate (eg to assess false negative rate) F1 score 2TP/(2TP+FP+FN) A weighted average of precision and recall