Handling Imbalanced Datasets I work in the medical domain, so class imbalance is the rule and not the exception. While I know Python has packages for class imbalance, I don't see an option in Orange for e.g. a SMOTE widget. I have read other threads in Stack Exchange regarding this, but I have not found an answer to how to tackle class imbalance in Orange without resorting to Python programming. Thanks
You can add class_weights with dictionary containing class weights, e.g.:
class_weight = {0: 1., 1: 20.} 

While SMOTE can be used to synthesize new examples for the minority class (the process is called oversampling) in order to get equal weights.
For Orange please check this link.
Please, provide more information so that we can help you.