Give some situations where you will use an SVM over a RandomForest Machine Learning algorithm and vice-versa?
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Answer / Deepa
SVM is useful for classification problems with a small number of features, or when the data is not linearly separable. It performs well in high dimensional spaces and can handle large datasets effectively. In contrast, Random Forests are suitable for handling datasets with many correlated features, non-linear relationships, and missing values. They also provide measures of variable importance.
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