【sklearn】【Decision Trees】【1.10】

 1. ExtraTree: http://blog.csdn.net/xbmatrix/article/details/69488867?locationNum=10&fps=1

 

 

sklearn.tree 含义 样例
tree.DecisionTreeClassifier 决策树分类算法 
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
import numpy as np
clf = DecisionTreeClassifier()
trainX = [[0,0], [0,1], [0,2], [1,0], [1,1], [1,2]]
trainY = [0,0,0,1,1,1]
testX = [[0,4],[1,4]]
clf.fit(trainX, trainY)
print clf.predict(testX)

output:
[0 1]
tree.DecisionTreeRegressor 决策树回归算法 
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeRegressor
import numpy as np
clf = DecisionTreeRegressor()
trainX = [[0,0], [0,1], [0,2], [1,0], [1,1], [1,2]]
trainY = [0,0,0,1,1,1]
testX = [[0,4],[1,4]]
clf.fit(trainX, trainY)
print clf.predict(testX)

output:
[ 0.  1.]
tree.ExtraTreeClassifier 极端随机数分类算法 
from sklearn.datasets import load_iris
from sklearn.tree import ExtraTreeClassifier
import numpy as np
clf = ExtraTreeClassifier()
trainX = [[0,0], [0,1], [0,2], [1,0], [1,1], [1,2]]
trainY = [0,0,0,1,1,1]
testX = [[0,4],[1,4]]
clf.fit(trainX, trainY)
print clf.predict(testX)

output:
[0 1]
tree.ExtraTreeRegressor 极端随机数回归算法 
from sklearn.datasets import load_iris
from sklearn.tree import ExtraTreeRegressor
import numpy as np
clf = ExtraTreeRegressor()
trainX = [[0,0], [0,1], [0,2], [1,0], [1,1], [1,2]]
trainY = [0,0,0,1,1,1]
testX = [[0,4],[1,4]]
clf.fit(trainX, trainY)
print clf.predict(testX)

output:
[ 0.  1.]
tree.export_graphviz 导出一个DOT格式的决策树 
from sklearn.datasets import load_iris
from sklearn import tree

clf = tree.DecisionTreeClassifier()
iris = load_iris()

clf = clf.fit(iris.data, iris.target)
tree.export_graphviz(clf, out_file='tree.dot')
     

 

posted @ 2018-03-18 00:04  aclove  阅读(96)  评论(0)    收藏  举报