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Wiki/Machine Learning/Imbalanced Data.md
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@@ -17,4 +17,6 @@ The synthetic minority oversampling technique (SMOTE) is used to generate synthe
5. Repeat 5. Repeat
![[SMOTE.excalidraw]] ![[SMOTE.excalidraw]]
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. 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 ### 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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