diff --git a/Wiki/Machine Learning/Model Validation and Evaluation.md b/Wiki/Machine Learning/Model Validation and Evaluation.md
index 53efa97..059e578 100644
--- a/Wiki/Machine Learning/Model Validation and Evaluation.md	
+++ b/Wiki/Machine Learning/Model Validation and Evaluation.md	
@@ -69,4 +69,5 @@ When using the metric in a binary classification problem with a 2x2 confusion ma
 $$
 \kappa = \frac{{2 \times (TP \times TN - FN \times FP)}}{(TP + FP) \times (FP + TN) + (TP + FN) \times (FN + TN)}
 $$
-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.
\ No newline at end of file
+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