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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

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.

### 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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