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