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Table 13 Efficiency metrics description

From: To supervise or to self-supervise: a machine learning based comparison on credit supervision

Efficiency metrics

Description

Formulae

Accuracy

Number of correct predictions to total number of samples ratio

\(\frac{True\, Positives\,+\,True\, Negatives}{Total \,number\, of \,samples}\)

Precision

Proportion of correct positive predictions, in relation to the total of positive predictions

\(\frac{True \,Positives}{False\, Positives\,+\,True \,Positives}\)

F1 Score

Harmonic mean between precision and recall

\(2*\frac{1}{\frac{1}{precision}+\frac{1}{recall}}\)

True positive rate (recall or sensitivity)

Proportion of correct positive predictions, in relation to all relevant samples, i.e., all positive samples

\(\frac{True \,Positives}{True \,Positives\,+\,False \,Negatives}\)

False positive rate (specificity)

Proportion of negative samples mistakenly predicted as positive, in relation to all negative samples

\(\frac{False\, Positives}{False\, Positives\,+\,True \,Negatives}\)

False negative rate

Proportion of positive samples mistakenly predicted as negative, in relation to all relevant samples, i.e., all positive samples

\(\frac{False \,Negative}{False \,Negative\,+\,True \,Positive}\)