diff --git a/Wiki/Machine Learning/Model Validation and Evaluation.md b/Wiki/Machine Learning/Model Validation and Evaluation.md
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@@ -75,4 +75,8 @@ ROC stands for the Receiver-operating characteristic curve. The graph is found b
 ![[rocCurve.excalidraw]]
 A perfect classifier is represented by a point at $(0, 1)$ which means that every prediction is correct (no false positives). A straight line from $(0, 0)$ to $(1, 1)$ represents a random classifier (same number of true and false positives). Curves will generally look like the green or blue ones with blue performing better than a random classifier and green performing worse.
 
-The area under the ROC curve (AUC) represents the probability that given random positive and negative examples it will rank the positive example above the negative one. A perfect classifier has a AUC of 1.0 meaning that it will always put a positive instance above a negative one thus separating the two classes.
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+The area under the ROC curve (AUC) represents the probability that given random positive and negative examples it will rank the positive example above the negative one. A perfect classifier has a AUC of 1.0 meaning that it will always put a positive instance above a negative one thus separating the two classes.
+### Metric Usage
+There are many different factors that need to be taken into account when choosing a metric to use to evaluate a model.
+
+One of the biggest ones is whether the dataset is balanced or not. If the dataset is imbalanced accuracy is not a good metric as it can reward a model doing well on a majority class and poorly on a minority class. This is especially problematic in scenarios such as predicting a disease where false negatives are extremely detrimental.
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