Training data is just one contributor to a successful classification. If your rasters are not representing the classes to be detected, increasing the number of training data won’t help.
Please have a look at this discussion on factors affecting the result of random forest classifications: Number of training samples at Random forest classifier
How much input rasters do you use?
Here are some suggestions on how to increase your feature space: Classification of GRD product - #2 by ABraun
About the interpretation:
This article gives a nice overview on the difference of accuracy and precision, as well as true positive/negative and false positive/negative: Sensitivity and specificity - Wikipedia