# Does GBM classification suffer from imbalanced class sizes?

I'm dealing with a supervised binary classification issue. I'd like to use the GBM package to classify individuals as uninfected/infected. I have 15 times more uninfected than infected individuals.

I was wondering if GBM models suffer in the case of imbalanced class sizes? I didn't find any references answering this question.

I tried to adjust the weights by assigning a weight of 1 to the uninfected individuals and a weight of 15 to the infected, but I obtained poor results.

-
(side note) It would be helpful if you provided what GBM stands for and a link to the package. – Memming Aug 10 at 12:58
Which loss function are you using for your gradient boosting model? When it comes to imbalanced classes, I've seen poor performance when I've used mean absolute error because it seems to favor the most common class. When I used mean squared error the performance improved substantially – Spicysheep Aug 12 at 18:38

In my experience, GBM does indeed suffer from imbalanced class sizes. I have had good success using SMOTE sampling, which creates synthetic data while oversampling the minority class. You can find it in the DMwR package.

-

I think your data is similar to Secom data on which I have worked in past and faced lot of difficulties. Following is what I have tried:

• Different sampling techniques
• Different classifiers like Random Forest, ANN, GBM, Ensemble methods, etc.

I've also tried 1-Class SVM which has given better results as compared to others like adaboost, Random Forest. You can try that as well.

And I can see you've asked this question 1 year back so if you've found the best way then kindly post it here so that I can get help from it to get better accuracy.

-