Imbalance Class Classification using Random Forest

@TarunSingh wrote:

Hi,

I have a dataset that has highly unbalanced class (binary outcome). In order to fit a model I know we have to make them balance by over sampling and under sampling, but I have 3 specific questions

1. Is Random Forest not resistent to unbalanced class? The reason being they already use bootstrapping to create multiple trees. If not then which algorithm is better than RF
2. How can we implement k fold cross validation and over sampling using SMOTE in python
3. Can I use StratifiedSampling while splitting dataset?

Please help. Thanks in advance

Tarun Singh

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