Preferred approaches for imbalanced data
I think it mostly depends on your dataset type! are you dealing with text? or image? or... and your features will tell which option is the best fit for your case....but according to my experience in most of the cases, option 1 and 2 besides they depend on your dataset and power of your features they need to be judge based on your model high bias or variance and they should inform you they are good or no! you need to do some experiment to figure out them or know your dataset well to find out adding or reducing dataset will affect your model performance or not!

and what I like to tell is try to use upsampling and downsampling methods same time to make your dataset balanced in a fair way(kinda)!....in this case (87% class 0 and 13% class 1)....upsample class 1 and downsample class 0! how much you need to upsample or how much downsample it is all your choice and definition of fairness in your dataset! and this definition could differ!