class sklearn.ensemble.RandomForestClassifier(n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, class_weight=None)
From my experience, there are three features worth exploring with the sklearn RandomForestClassifier, in order of importance:
n_estimators is not really worth optimizing. The more estimators you give it, the better it will do. 500 or 1000 is usually sufficient.
max_features is worth exploring for many different values. It may have a large impact on the behavior of the RF because it decides how many features each tree in the RF considers at each split.
criterion may have a small impact, but usually the default is fine. If you have the time, try it out.
Make sure to use sklearn's GridSearch (preferably GridSearchCV, but your data set size is too small) when trying out these parameters.
If I understand your question correctly, though, you only have 9 samples and 3 classes? Presumably 3 samples per class? It's very, very likely that your RF is going to overfit with that little amount of data, unless they are good, representative records.