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vault backup: 2026-06-11 20:43:24

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Wiki/Machine Learning/Model Validation and Evaluation.md
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@@ -70,4 +70,7 @@ $$
\kappa = \frac{{2 \times (TP \times TN - FN \times FP)}}{(TP + FP) \times (FP + TN) + (TP + FN) \times (FN + TN)} \kappa = \frac{{2 \times (TP \times TN - FN \times FP)}}{(TP + FP) \times (FP + TN) + (TP + FN) \times (FN + TN)}
$$ $$
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 ### ROC and AUC
ROC stands for the Receiver-operating characteristic curve. The graph is found by plotting the true positive rate on the y-axis and the false positive rate on the x-axis as the threshold changes. This is a parametric graph with the threshold as the parameter.
![[rocCurve.excalidraw]]
A perfect classifier is represented by a point at $(0, 1)$ which means that every prediction is correct (no false positives)