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vault backup: 2026-06-11 20:33:15

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Wiki/Machine Learning/Model Validation and Evaluation.md
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@@ -69,4 +69,5 @@ When using the metric in a binary classification problem with a 2x2 confusion ma
$$ $$
\kappa = \frac{{2 \times (TP \times TN - FN \times FP)}}{(TP + FP) \times (FP + TN) + (TP + FN) \times (FN + TN)} \kappa = \frac{{2 \times (TP \times TN - FN \times FP)}}{(TP + FP) \times (FP + TN) + (TP + FN) \times (FN + TN)}
$$ $$
When using it in this context a value of 1 represents a perfect classifier (model and reality are in perfect agreement) and 0 represents the same accuracy as random chance. The value can also go down to -1 which represents complete disagreement and worse performance than random chance. When using it in this context a value of 1 represents a perfect classifier (model and reality are in perfect agreement) and 0 represents the same accuracy as random chance. The value can also go down to -1 which represents complete disagreement and worse performance than random chance.
### AUC-ROC Curve