The accuracy in SNAP determines how well the Random Forest classifier matches your training data. Even if it would be 95%, there could be lots of errors in your map when the training data were not representing all of the available classes. It is therefore required to also perform an independent accuracy assessment of your classified data.
For this, you need to digitize further points (independently from the output) and compare how well the classes match. e.g. how many of 100 points of the class wetland were classified correctly?
SNAP is not able to do this at the moment, but I explained it here at more detail: GLCM worsening accuracy results?