Cross validation for unbalanced dataset using Orange data mining tool I am using the Orange data mining tool to build and analyze models (decision tree, ANN, ...) predicting customer churn. As this is an imbalanced class problem (10% churn, 90% not churn), I need to oversample within the cross validation. However, I am not totally able to implement this by myself. Is there anyone with some Orange knowledge that could help me?
Thank you!
Orange does not have over/undersampling. Our reasoning is that if you model a problem with 10% positive class, than you should not train the model with 50:50 class distribution - it will not reflect the real life. However, there's an option in Orange in LogReg and Random Forest to balance class distribution, which considers class distribution when building a model.