结合scikit学习中的随机森林模型

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了训练。我怎样才能做到这一点？

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

我想将big_rf所有树木合并成一个500棵树模型

Answer 1

我相信可以通过修改RandomForestClassifier对象的estimators_和n_estimators属性来实现。森林中的每棵树都存储为DecisionTreeClassifier对象，这些树的列表存储在estimators_属性中。为了确保不存在不连续性，更改中的估计量也很有意义n_estimators。

这种方法的优点是，您可以跨多台机器并行构建一堆小森林并将其组合。

这是使用虹膜数据集的示例：

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)