# 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

• Good news! Class imbalance is not a problem! stats.stackexchange.com/questions/357466/…
– Dave
Feb 22 at 1:20
• @Dave I would not recommend the linked answer to any beginner with regards to imbalanced datasets since the author reveals some prejudice against imbalanced data handling techniques in his comments. Accordingly, one should have some background knowledge to put his reasoning into perspective. Feb 22 at 12:19

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.