Python '；功能'；对象没有属性'；列车试验分离和x27；

我是Python中实现机器学习的新手，目前正在YouTube教程之后尝试KNN分类。这是代码

import numpy as np
#from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_validate
import pandas as pd

df=pd.read_csv('breast-cancer-wisconsin.data.txt')
df.replace('?', -99999, inplace=True)
df.drop(['id'],1,inplace=True)

X=np.array(df.drop(['class'],1))
y=np.array(df['class'])

X_train, X_test, y_train, y_test=cross_validate.train_test_split(X,y,test_size=0.2)

我得到以下错误：-

X_train, X_test, y_train, y_test=cross_validate.train_test_split(X,y,test_size=0.2)
AttributeError: 'function' object has no attribute 'train_test_split'

我试着导入train\u test\u split作为

 from sklearn.model_selection import train_test_split

---

但后来我得到了同样的错误。感谢您的帮助。谢谢

列车测试分割

是一个单独的模块（），不能与

交叉验证

结合使用；此处的正确用法是（假设scikit学习v0.20）：

---

sklearn.cross_验证在版本0.20中已弃用

---

使用sklearn.model_selection.train_test_split

从2021年4月起，我正在观看相同的教程和正确答案，如下所示：

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)

下面是本课程的完整代码：

import numpy as np
from sklearn import preprocessing, neighbors
from sklearn.model_selection import train_test_split
import pandas as pd

df= pd.read_csv('breast-cancer-wisconsin.data')
# print(df.head())

df.replace('?', -99999, inplace=True)

df.drop(['id'], 1, inplace=True)
# print(df.columns)


X= np.array(df.drop(['class'],1))
y= np.array(df['class'])

X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)

clf = neighbors.KNeighborsClassifier()
clf.fit(X_train,y_train)


accuracy= clf.score(X_test,y_test)
print(accuracy)

import numpy as np
from sklearn import preprocessing, neighbors
from sklearn.model_selection import train_test_split
import pandas as pd

df= pd.read_csv('breast-cancer-wisconsin.data')
# print(df.head())

df.replace('?', -99999, inplace=True)

df.drop(['id'], 1, inplace=True)
# print(df.columns)


X= np.array(df.drop(['class'],1))
y= np.array(df['class'])

X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)

clf = neighbors.KNeighborsClassifier()
clf.fit(X_train,y_train)


accuracy= clf.score(X_test,y_test)
print(accuracy)