Imbalanced data: undersampling or oversampling?
Undersampling:
Undersampling is typically performed when we have billions (lots) of data points and we don’t have sufficient compute or memory(RAM) resources to process the data. Undersampling may lead to worse performance as compared to training the data on full data or on oversampled data in some cases. In other cases, we may not have a significant loss in performance due to undersampling.
Undersampling is mainly performed to make the training of models more manageable and feasible when working within a limited compute, memory and/or storage constraints.
Oversampling:
oversampling tends to work well as there is no loss of information in oversampling unlike undersampling.
I have binary classification problem where one class represented 99.1% of all observations (210 000). As a strategy to deal with the imbalanced data, I choose sampling techniques. But I don't know what to do: undersampling my majority class or oversampling the less represented class. 
If anybody have an advise?
Thank you.
P.s. I use random forest algorithm from sklearn.