I have some data which I use SVC models with 10 fold cross validation and a parameter grid search on (scikit.learn). I observed that the predictions of some folds have low accuracy, whereas remained folds have better accuracy. With different C and gamma parameters of SVC, the prediction of previously low folds improves, but the better accuracies of other folds become worse than before. From this I derive, that if I could somehow combine these two or three models with different parameters, it will overall get better results. As far as know, Python has ensemble classifiers, but it allows only one base model, but in my case there will be more than one SVC models which should have different parameters.
My question is: is it possible to combine different SVC models with different parameters in Python and/or scikit.learn? If so, how can I do it?