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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


Answers to Questions (FAQ)

What is a confusion matrix? (Definition)

A confusion matrix, also called an error matrix, is an evaluation tool often presented as a table of 4 boxes containing 4 essential values to statistically measure/evaluate a result. Generally, from a classification model and/or an artificial intelligence algorithm.

The 4 values are:

— the number of true positives (TP): the number of observations correctly predicted as positive.

— the number of false positives (FP): the number of actual negative observations incorrectly predicted as positive.

— the number of true negatives (TN): the number of observations correctly predicted as negative.

— the number of false negatives (FN): the number of actual positive observations incorrectly predicted as negative.

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 many 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, also called precision, 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.

How does the Confusion Matrix help improve models?

By providing a detailed view of classification errors, the Confusion Matrix helps to understand the strengths and weaknesses of the model, thus strengthening the interpretability of the results.

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Confusion Matrix on dCode.fr [online website], retrieved on 2024-06-14, https://www.dcode.fr/confusion-matrix

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