我对我的数据集进行了分类，它有超过46列和约500,000行。
import numpy as np
import pandas as pd
from sklearn.cross_validation import train_test_split
%matplotlib inline
我在这里导入了数据集
df=pd.read_csv('Terror.csv', sep=',')
df.head()
在这里，我将列分为目标和训练。
column_target=['success']
column_train=['iyear','country','region','latitude','longitude','specificity','vicinity','doubtterr','alternative','attacktype1','multiple','targtype1','natlty1','gname_id']
x=df[column_train]
y=df[column_target]
我用NA填充了空行。
x['latitude']=x['latitude'].fillna(x['latitude'].median())
x['longitude']=x['longitude'].fillna(x['longitude'].median())
x['doubtterr']=x['doubtterr'].fillna(x['doubtterr'].median())
x['alternative']=x['alternative'].fillna(x['alternative'].median())
x['natlty1']=x['natlty1'].fillna(x['natlty1'].median())
x['natlty1']=x['natlty1'].fillna(x['natlty1'].median())
在这里，我将我的X和Y分为测试集和训练集。
x_train, x_test, y_train, y_test=train_test_split(x, y, test_size=0.33,
random_state=42)
尝试生成决策树图。
from sklearn import tree
Tree=tree.DecisionTreeClassifier()
Tree=Tree.fit(x_train,y_train)
import pydotplus
from IPython.display import Image
dot_data= tree.export_graphviz(Tree, out_file=None,feature_names=x_train.columns,class_names=y_train.columns,filled=True,rounded=True,special_characters=True,max_depth=10)
graph= pydotplus.graph_from_dot_data(dot_data)
Image(graph.create_png())
但是它给我返回了这个错误。
IndexError Traceback (most recent call last)
<ipython-input-42-1ac22988949f> in <module>()
1 import pydotplus
2 from IPython.display import Image
----> 3 dot_data= tree.export_graphviz(Tree, out_file=None,
feature_names=x_train.columns,class_names=y_train.columns,
filled=True,rounded=True,special_characters=True,max_depth=10)
4 graph= pydotplus.graph_from_dot_data(dot_data)
5 Image(graph.create_png())
C:\Users\dell\Anaconda2\lib\site-packages\sklearn\tree\export.pyc in
export_graphviz(decision_tree, out_file, max_depth, feature_names, class_names, label, filled, leaves_parallel, impurity, node_ids, proportion, rotate, rounded, special_characters)
431 recurse(decision_tree, 0, criterion="impurity")
432 else:
--> 433 recurse(decision_tree.tree_, 0, criterion=decision_tree.criterion)
434
435 # If required, draw leaf nodes at same depth as each other
C:\Users\dell\Anaconda2\lib\site-packages\sklearn\tree\export.pyc in
recurse(tree, node_id, criterion, parent, depth)
319 out_file.write('%d [label=%s'
320 % (node_id,
--> 321 node_to_str(tree, node_id, criterion)))
322
323 if filled:
C:\Users\dell\Anaconda2\lib\site-packages\sklearn\tree\export.pyc in
node_to_str(tree, node_id, criterion)
284 node_string += 'class = '
285 if class_names is not True:
--> 286 class_name = class_names[np.argmax(value)]
287 else:
288 class_name = "y%s%s%s" % (characters[1],
C:\Users\dell\Anaconda2\lib\site-packages\pandas\indexes\base.pyc in
__getitem__(self, key)
1421
1422 if is_scalar(key):
-> 1423 return getitem(key)
1424
1425 if isinstance(key, slice):
IndexError: index 1 is out of bounds for axis 0 with size 1
我不知道我的代码哪里出了问题，导致它没有给我那个决策树图像。