Let say I'm trying to apply CNN for image classification. There are lots of different models to choose and we can try an ensemble, but given a limit amount of resources, it does not allow to try everything.
Is there a theory behind which model is good for a classification task for the convolutional neural network?
Right now, I'm just taking an average of three predictions.
predictions_model = [y_pred_xceptionAug,y_pred_Dense121_Aug,y_pred_resnet50Aug] predictions = np.mean(predictions_model,axis=0)
But each model's performance is different. Is there better way for ensemble methods?