我正在学习如何在python中使用决策树。我修改了一个例子来导入一个csv文件，而不是使用这个站点的iris数据集：

http://machinelearningmastery.com/get-your-hands-dirty-with-scikit-learn-now/

代码：

import numpy as np
import urllib
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn import datasets
from sklearn import metrics

# URL for the Pima Indians Diabetes dataset (UCI Machine Learning Repository)
url = "http://goo.gl/j0Rvxq"
# download the file
raw_data = urllib.urlopen(url)
# load the CSV file as a numpy matrix
dataset = np.loadtxt(raw_data, delimiter=",")
#print(dataset.shape)
# separate the data from the target attributes
X = dataset[:,0:7]
y = dataset[:,8]
# fit a CART model to the data
model = DecisionTreeClassifier()
model.fit(dataset.data, dataset.target)
print model

错误：

Traceback (most recent call last):
  File "DatasetTest2.py", line 24, in <module>
    model.fit(dataset.data, dataset.target)
AttributeError: 'numpy.ndarray' object has no attribute 'target'

我不知道为什么会发生这种错误。如果我使用示例中的iris数据集，它就可以正常工作。最终，我需要能够在csv文件上执行决策树。

我还尝试了以下代码，也会导致相同的错误：

# Import Python Modules
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn import datasets
from sklearn import metrics
import pandas as pd
import numpy as np

#Import Data
raw_data = pd.read_csv("DataTest1.csv")
dataset = raw_data.as_matrix()
#print dataset.shape
#print dataset
# separate the data from the target attributes
X = dataset[:,[2,3,4,7,10]]
y = dataset[:,[1]]
#print X
# fit a CART model to the data
model = DecisionTreeClassifier()
model.fit(dataset.data, dataset.target)
print model