Random Forest Classifier Probabilities
What do you mean by 'all over the place'? Have you tried actually using scoring measures such as accuracy scores, AUC, etc? These will play a vital role in determining how your model is performing. I'd suggest ROC AUC as to start off since accuracy will be pretty misleading due to the class imbalance.
The class weights you used make little sense. You usually would use something like n_samples / (n_classes * np.bincount(y)) for your weighting, but since sklearn provides automatic balancing of classes, you should probably use that by setting class_weights='balanced': http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html