我想使用sklearn DecisionTreeClassifier.
我比较两个列表,ListOnePar
有浮点值,timelist
只有字符串。我总是得到同样的错误。我搜索了网络,但我找不到任何可以帮助我的东西。我只看到可以在两个列表之间进行比较(一个带有浮点数,另一个带有字符串。)
这不是另一个问题的重复,在另一个问题中,错误是完全不同的,整个程序是不同的。
这是错误:
Pred1=tree.DecisionTreeClassifier()
AttributeError: 'float' object has no attribute 'DecisionTreeClassifier'
这是代码:
from sklearn import tree
ListOnePar=[]
for child in tree1.get_children(id1):
ListTwoPar=[]
one=round(float(tree1.item(child,"values")[1]),2)
two=round(float(tree1.item(child,"values")[2]),2)
tree=round(float(tree1.item(child,"values")[3]),2)
four=round(float(tree1.item(child,"values")[5]),1)
five=round(float(tree1.item(child,"values")[6]),1)
ListTwoPar.append(one)
ListTwoPar.append(two)
ListTwoPar.append(tree)
ListTwoPar.append(four)
ListTwoPar.append(five)
ListOnePar.append(ListTwoPar)
timelist=[]
for child in tree1.get_children(id1):
time=tree1.item(child,"values")[7]
timelist.append(time)
Pred1=tree.DecisionTreeClassifier()
Pred1=Pred1.fit(ListOnePar,time)
size=float(PredSizeEntry.get())
time=float(PredTimeEntry.get())
cost=float(PredCostEntry.get())
level=float(PredLevelEntry.get())
subcontractors=float(PredSubcontractorsEntry.get())
ListForPrediction1=[]
ListForPrediction2=[]
ListForPrediction2.insert(0,size)
ListForPrediction2.insert(1,time)
ListForPrediction2.insert(2,cost)
ListForPrediction2.insert(3,level)
ListForPrediction2.insert(4,subcontractors)
ListForPrediction1.append(ListForPrediction2)
prediction1=Pred1.predict(ListForPrediction1)
print(prediction1[0])
答案 0 :(得分:4)
tree
tree
或tree
变量,因为你要覆盖tree
浮动将变量名称更改为three
for child in tree1.get_children(id1):
ListTwoPar=[]
one=round(float(tree1.item(child,"values")[1]),2)
two=round(float(tree1.item(child,"values")[2]),2)
tree=round(float(tree1.item(child,"values")[3]),2) # <===== variable to be changed from tree to three
four=round(float(tree1.item(child,"values")[5]),1)
five=round(float(tree1.item(child,"values")[6]),1)
您在计算tree
时将tree=round(float(tree1.item(child,"values")[3]),2)
设为浮点数,因此您收到错误:AttributeError: 'float' object has no attribute 'DecisionTreeClassifier'