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Confusion Matrix

Tool to calculate statistical data (sensitivity, specificity, precision, predictive value, etc.) from true positives, true negatives, false positives, false negatives values, also called confusion matrix.

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Confusion Matrix -

Tag(s) : Data Processing

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# Confusion Matrix

## Confusion Matrix

True Positive (TP) : item declared TRUE, and in reality TRUE

False Positive (FP) or Type I error: item declared TRUE but in reality FALSE

True Negative (TN): item declared FALSE, and in reality FALSE

False Negative (FN) or Type II error: item declared FALSE but in reality TRUE

### What is a confusion matrix?

A confusion matrix, also called an error matrix, is an array of 4 boxes comprising 4 essential values to statistically evaluate a result. Usually, resulting from a classification and / or an artificial intelligence algorithm.

The 4 values are:

— the number of true positives (TP)

— the number of false positives (FP)

— the number of true negatives (TN)

— the number of false negatives (FN)

Example: TP:99,FP:1,TN:95:FN:5

### How to evaluate a confusion matrix?

The 4 values of the confusion matrix make it possible to calculate 8 other values of statistical interest:

— the rate of true TPR positives, also called sensitivity or recall TPR = TP / (TP + FN)

— the rate of true FPR negatives, also called specificity FPR = TN / (FP + TN)

— the positive predictive value PPV = TP / (TP + FP)

— the negative predictive value NPV = TN / (TN + FN)

— the rate of false positives FPR = FP / (FP + TN)

— the rate of false negatives FNR = FN / (FN + TP)

— the rate of false discoveries FDR = FP / (FP + TP)

— the rate of false omissions FOR = FN / (FN + TN)

In addition, additional indicators can be useful such as accuracy or F1 score.

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