决策树（Decision Tree）应用

1.python机器学习的库：scikit-learn

   1.1 特性：

        简单高效的数据挖掘和机器学习分析；对所有用户开放，根据不同需求高度可重用性；基于Numpy,Scipy和matplotlib

   1.2 覆盖问题领域：

       分类；回归；聚类；降维；模型选择；预处理

2.使用scikit-learn

  安装scikit-learn:pip,easy_install,windows installer

 安装必要的package:numpy,scipy和matplotlib,可使用anaconda(包含numpy,scipy等科学计算常用的package)

我用的是Pycharm2017+anaconda3(包含了机器学习的库scikit-learn,和常用的package,可在cmd->conda list中查看)

3.例子：

RID	age	income	student	credit_rating	class:buys_computer
1	youth	high	no	fair	no
2	youth	high	no	excellent	no
3	middle_aged	high	no	fair	yes
4	senior	medium	no	fair	yes
5	senior	low	yes	fair	yes
6	senior	low	yes	excellent	no
7	middle_aged	low	yes	excellent	yes
8	youth	medium	no	fair	no
9	youth	low	yes	fair	yes
10	senior	medium	yes	fair	yes
11	youth	medium	yes	excellent	yes
12	middle_aged	medium	no	excellent	yes
13	middle_aged	high	yes	fair	yes
14	youth	medium	no	excellent	no

安装Graphviz:http://www.graphviz.org/

配置环境变量：cmd->env

转化dot至pdf可视化决策树：安装了Graphviz,但一直没在cmd中转化出来，求指点！

源码：

3.例子

from sklearn.feature_extraction import DictVectorizer
import csv
from sklearn import preprocessing
from sklearn import tree
from sklearn.externals.six import StringIO

# 读取文件
with open(r'E:\PycharmProjects\python\Decesiopn Tree\Allelectronicsdate.csv', 'r') as allElectronicsData:
    allElectronicsData = open(r'E:\PycharmProjects\python\Decesiopn Tree\Allelectronicsdate.csv', 'r')
    reader = csv.reader(allElectronicsData)
    headers = next(reader)
    print(headers)

featureList = []
labelList = []
for row in reader:
    labelList.append(row[len(row) - 1])
    rowDict = {}
    for i in range(1, len(row) - 1):
        rowDict[headers[i]] = row[i]
    featureList.append(rowDict)
print(featureList)
allElectronicsData.close()

# 转化数据为sklearn要求的数据
vec = DictVectorizer()
dummyX = vec.fit_transform(featureList).toarray()
print("dummyX:" + str(dummyX))
print(vec.get_feature_names())
print("labelList:" + str(labelList))

lb = preprocessing.LabelBinarizer()
dummyY = lb.fit_transform(labelList)
print("dummyY:" + str(dummyY))
## 决策树处理
clf = tree.DecisionTreeClassifier(criterion='entropy')
clf = clf.fit(dummyX, dummyY)
print("clf:" + str(clf))
## 将决策树输出位dot文件
with open("E:\PycharmProjects\python\Decesiopn Tree\DecisionTree.dot", 'w') as f:
    f = tree.export_graphviz(clf, feature_names=vec.get_feature_names(), out_file=f)
oneRowX = dummyX[0, :]
print("oneRowX:" + str(oneRowX))
## 给定新的数据进行预测
newRowX = oneRowX
newRowX[0] = 1
newRowX[2] = 0
print("newRowX:" + str(newRowX))

predictedY = clf.predict(newRowX)
print("predictedY:" + str(predictedY))

生成的决策树：