1
$\begingroup$

I am using Orange to predict customer churn and compare different learners based on accuracy, F1, etc.

As my problem is unbalanced (10% churn - 90% not churn), I want to oversample. However, when using orange, this is not possible to do the oversampling within the cross-validation (test & score block).

Therefore, I want to, based on my input data, generate first 10 folds (stratified - where the distribution 10 % churn / 90 % not churn) is preserved. Then, oversample within each fold to get 50 - 50 distribution. Then, add for each instance the fold number as a feature. Lastly, within the test & score block, do cross validation by feature, namely the fold number. I think I have to implement this myself by using a Python script. Is there anyone that could help me doing this?

Thank you! Emma

Improve this question
$\endgroup$

0

Reset to default

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.