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.
Confusion Matrix - dCode
Tag(s) : Data Processing
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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
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.
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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Cite as source (bibliography):
Confusion Matrix on dCode.fr [online website], retrieved on 2024-12-04,