"Training classifier (SVM or Logit) with unbalanced training data"

noah977

Post removed.

This was answered previously, but I missed the answer.

The EquallabelWeighting operator solves the problem

steffen

Hello Noah

noah977 wrote:
I've read that this makes sense because It is less error (safer) for the classifier to simply mark everything negative with such unbalanced data.

The problem here is that you are not interested in the prediction, but in the confidence. I guess you already know that the prediction is calculated by applying a threshold on the confidence (per default 0.5, but adjustable in RapidMiner (search for "threshold")). 
The confidence is representing a scoring or ranking of your items. To estimate the quality of your classification you can look at the ROC-curce or AUC values. The AUC values for instance cannot be biased by class skew, meanwhile the Accuracy can easily be tricked this way (as you mentioned above).

To gain the mentioned probabilities: The calculated confidences are approximations for the probabilities. To better this approximations you use calibration methods. As far as I know the only method implemented is platt scaling, which is the best calibration method for the output of SVM-Classificators and a moderate good method for the output of other classificators.

Feel free to ask if something I explained is not clear

hope this was helpful

kind regards,

Steffen

PS: Indeed, changing the true class distribution can hurt  the performance