User Manual

ASUS, Inc
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2.2.4.10 Confusion Matrix
 Refer to the figure below for the definition and explanation of Confusion
Matrix.
 True Positives (TP): Predicted target event a Positive, and the actual
event is a positive.
 True Negatives (TN): Predicted target event a Negative, and the actual
event is a negative.
 False Positives (FP): Predicted target event a Positive, and the actual
event is a negative.
 False Negatives (FN): Predicted target event a Negative, and the
actual event is a positive.
 As mentioned above, we can use TP, TN, FP and FN values to calculate
accuracy, recall, loss, detection and overkill:
 Accuracy = (TP+TN) / (TP+FP+FN+TN)
 Accuracy is the ratio of total sum of all true events predicted
over total sum of all predicted events.
 Precision = TP / (TP+FP)