试图从scikit-learn合奏中打印出森林的决策树

Question

我试图从scikit-learn ensemble打印出森林的决策树:例如对于DecisionTreeClassifier,我会使用:

from sklearn import tree  
clf = tree.DecisionTreeClassifier( criterion ='entropy', max_depth = 3,    
min_samples_leaf = 
clf = clf.fit( X_train, y_train) #Input this to analyze the training set.

import pydot, StringIO
dot_data = StringIO.StringIO() 
tree.export_graphviz( clf, out_file = dot_data,    
feature_names =[' age', 'sex', 'first_class', 'second_class', 'third_class'])
graph = pydot.graph_from_dot_data( dot_data.getvalue())
graph.write_png('visualtree.png') 
from IPython.core.display import Image
Image( filename =visualtree.png')

我为Random Forest Regressor尝试了类似的方法(见下文并得到错误)

# Fit regression model
from sklearn.ensemble import RandomForestRegressor
rfr_1 = RandomForestRegressor(n_estimators=10, max_depth=5)
rfr_1.fit(X, y)

from sklearn.ensemble import*
import pydot, StringIO
dot_data = StringIO.StringIO() 
ensemble.export_graphviz( rf1, out_file = dot_data,    
feature_names =[' Temperature', 'Translator Bacteria'])
graph = pydot.graph_from_dot_data( dot_data.getvalue())
graph.write_png('fish.png') 
from IPython.core.display import Image

图片(filename ='fish.png')

文件"randomforestregressor.py",第45行,在ensemble.export_graphviz中(rf1,out_file = dot_data,
NameError:name'ensemble '未定义

我怎么做到这一点？谢谢!

Answer 1

错误消息非常明确:

File "randomforestregressor.py", line 45, in ensemble.export_graphviz( rf1, out_file = dot_data, NameError: name 'ensemble' is not defined

您可以访问ensemble脚本行45中命名的变量,但从不定义此类变量.在您的情况下,您可能希望该变量指向sklearn.ensemble包:

from sklearn import ensemble

但是,如果你这样做,你可能会得到一个包,AttributeError因为sklearn.ensemble包没有export_graphviz功能.

相反,您可能想要做的是通过迭代rfr_1.estimators_列表的元素并在每个树上调用包的export_graphviz方法,在林中为每个树生成一个图像sklearn.tree.

然而,在实践中,显示森林的树木通常是无用的.从业者通常会建造具有数百或数千棵树的随机森林,以获得良好的预测准确性.在这种情况下,目视检查许多树是不切实际的.