python - 在 scikit learn 中组合随机森林模型

Question

我有两个 RandomForestClassifier 模型，我想将它们组合成一个元模型。他们都使用相似但不同的数据进行训练。我该怎么做？

rf1 #this is my first fitted RandomForestClassifier object, with 250 trees
rf2 #this is my second fitted RandomForestClassifier object, also with 250 trees

我想创建 big_rf 并将所有树组合成一个 500 棵树模型

Answer 1

我相信这可以通过修改 RandomForestClassifier 对象的 estimators_ 和 n_estimators 属性来实现。森林中的每棵树都存储为一个 DecisionTreeClassifier 对象，这些树的列表存储在 estimators_ 属性中。为确保不存在不连续性，更改 n_estimators 中的估计器数量也是有意义的。

这种方法的优点是你可以在多台机器上并行构建一堆小森林并将它们组合起来。

这是一个使用鸢尾花数据集的例子:

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)