from sklearn import ensemble, datasets
from sklearn.externals import joblib
iris = datasets.load_iris()
X, y = iris.data, iris.target
clf = ensemble.RandomForestClassifier()
clf.fit(X, y)
joblib.dump(clf, 'rf-model.pkl')

from sklearn.externals import joblib
clf = joblib.load('rf-model.pkl')
clf.predict(Xnew)

int32_t digits_predict_tree_0(int32_t *features, int32_t features_length)
{
  if (features[36] < 32768) {
      if (features[57] < 65536) {
          return 9;
      } else {
          if (features[10] < 950272) {
              return 5;
  ....
}

int32_t digits_predict(int32_t *fe, int32_t features_length)
{
   _class = digits_predict_tree_1(fe, length); votes[_class] += 1;
   _class = digits_predict_tree_2(fe, length); votes[_class] += 1;
   ....
   return find_majority(votes);
}