我相信這可以通過修改RandomForestClassifier對象上的estimators_和n_estimators屬性來實作。林中的每個樹都存儲為DecisionTreeClassifier對象，這些樹的清單存儲在estimators_屬性中。為了確定不存在間斷性，改變n_estimators中的估計數也是有意義的。
這種方法的優點是，您可以在多台機器上并行地建構一堆小森林，并将它們組合起來。
下面是一個使用iris資料集的示例：from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import train_test_split
from sklearn.datasets import load_iris
def generate_rf(X_train, y_train, X_test, y_test):
rf = RandomForestClassifier(n_estimators=5, min_samples_leaf=3)
rf.fit(X_train, y_train)
print "rf score ", rf.score(X_test, y_test)
return rf
def combine_rfs(rf_a, rf_b):
rf_a.estimators_ += rf_b.estimators_
rf_a.n_estimators = len(rf_a.estimators_)
return rf_a
iris = load_iris()
X, y = iris.data[:, [0,1,2]], iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.33)
# in the line below, we create 10 random forest classifier models
rfs = [generate_rf(X_train, y_train, X_test, y_test) for i in xrange(10)]
# in this step below, we combine the list of random forest models into one giant model
rf_combined = reduce(combine_rfs, rfs)
# the combined model scores better than *most* of the component models
print "rf combined score", rf_combined.score(X_test, y_test)