Accuracy is calculated as the total number of two correct predictions TP TN

# Accuracy is calculated as the total number of two

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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 RAM MOHAN (+61406471624 (please text to watsapp) 2018 sem 1 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). RAM MOHAN (+61406471624 (please text to watsapp) 2018 sem 1 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 RAM MOHAN (+61406471624 (please text to watsapp)  #### You've reached the end of your free preview.

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