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Wiki/Machine Learning/Imbalanced Data.md

@@ -19,4 +19,6 @@ The synthetic minority oversampling technique (SMOTE) is used to generate synthe
  SMOTE can be helpful to increase the size of a minority dataset but has a few limitations. If the minority class is too small SMOTE can overgeneralize. It also does not work very well with categorical data and can create some data points with values that don't make sense for the feature. There is also no way for it to predict/generate outliers which could cause a model to overfit the data.
 ### Algorithm-Level
 At the level of the algorithm there are a few approaches that can be taken to minimize the negative effects of imbalanced data.
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+- Some models can weight different classes to prioritize one over another
+- The choice of model to use is important in handling imbalanced data
+	- Ensemble models such as random forests are good at handling imbalanced data