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
- 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
- How can we implement k fold cross validation and over sampling using SMOTE in python
- Can I use StratifiedSampling while splitting dataset?
Please help. Thanks in advance