Accuracy is calculated as the total number of two

Accuracy is calculated as the total number of two correct predictions (TP + TN) divided by the total
number of a dataset (P + N).

Confusion Matrix
Predicted No Predicted Yes
Actual No TN FP
Actual Yes FN TP
TP = True Positive Accuracy Rate = (TP+TN)/Total
FP = False Positive True Positive Rate = TP/Actual Yes
TN = True Negtive False Positive Rate = FP/Actual no
FN = False Negative Specificity = TN/Actual No
Sensitivity = True Positive Rate

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Sensitivity (Recall or True positive rate)
Sensitivity (SN) is calculated as the number of correct positive predictions divided by the total
number of positives. It is also called recall (REC) or true positive rate (TPR). The best sensitivity is
1.0, whereas the worst is 0.0.
Sensitivity is calculated as the number of correct positive predictions (TP) divided by the total number
of positives (P).

Specificity (True negative rate)
Specificity (SP) is calculated as the number of correct negative predictions divided by the total
number of negatives. It is also called true negative rate (TNR). The best specificity is 1.0, whereas
the worst is 0.0.
Specificity is calculated as the number of correct negative predictions (TN) divided by the total number
of negatives (N).

Precision (Positive predictive value)
Precision (PREC) is calculated as the number of correct positive predictions divided by the total
number of positive predictions. It is also called positive predictive value (PPV). The best precision is
1.0, whereas the worst is 0.0.
Precision is calculated as the number of correct positive predictions (TP) divided by the total number
of positive predictions (TP + FP).


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False positive rate
False positive rate (FPR) is calculated as the number of incorrect positive predictions divided by the
total number of negatives. The best false positive rate is 0.0 whereas the worst is 1.0. It can also be
calculated as 1 – specificity.
False positive rate is calculated as the number of incorrect positive predictions (FP) divided by the
total number of negatives (N).

Example
Predicted NO Predicted YES
Actual NO FS=4 TP=6
Actual YES TN=8 FP=2
Then, the calculations of basic measures are straightforward once the confusion matrix is created.
measure calculated value
Error rate ERR 6 / 20 = 0.3
Accuracy ACC 14 / 20 = 0.7
Sensitivity SN 6 / 10 = 0.6
True positive rate TPR
Recall REC
Specificity SP 8 / 10 = 0.8
True negative rate TNR
Precision PREC 6 / 8 =0.75
Positive predictive value PPV
False positive rate FPR 2 / 10 = 0.2

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