小小白在上手titanic,出了问题，求帮忙解答~
代码如下：

 import pandas
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold
#导入

titanic = pandas.read_csv("all/train.csv")
#print(titanic.head(3))
#print(titanic.describe())

#处理缺失数据
titanic["Age"] = titanic["Age"].fillna(titanic["Age"].median())
#print(titanic.describe())
titanic.loc[titanic["Sex"]=="male","Sex"]=0
titanic.loc[titanic["Sex"]=="female","Sex"]=1
titanic["Embarked"] = titanic["Embarked"].fillna('S')
titanic.loc[titanic["Embarked"]=="S","Embarked"]=0
titanic.loc[titanic["Embarked"]=="C","Embarked"]=1
titanic.loc[titanic["Embarked"]=="Q","Embarked"]=2
#print(titanic["Sex"].unique())
#print(titanic["Embarked"].unique())

#KFold
predictors = ["Pclass","Sex","SibSp","Parch","Fare","Embareked"]
alg = LinearRegression()
kf = KFold(titanic.shape[0],n_folds=3,random_state=1)
predictions = []
for train, test in kf:
    train_predictiors = (titanic[predictors].iloc[train,:])
    train_target = titanic["Survived"].iloc[train]
    alg.fit(train_predictiors,train_target)
    test_prdictions = alg.predict(titanic[predictors].iloc[test,:])
    predictions.append(test_prdictions)

错误如下：
Traceback (most recent call last):
File "F:/python项目/titanic.py", line 20, in
kf = KFold(titanic.shape[0],n_folds=3,random_state=1)
TypeError: init() got an unexpected keyword argument 'n_folds'

Process finished with exit code 1

非常感谢~

该问题是由于sklearn中模块的迁移和更新导致的
解决方法一：不理会版本情况
直接将

from sklearn.model_selection import KFold

改为

from sklearn.cross_validation import KFold

解决方法二:
输入：

import sklearn
print(help(sklearn.model_selection.KFold())

查看帮助文档

sklearn.model_selection.KFold()可以设置三个参数：
1. n_splits : int型, default=3，代表交叉验证的折数，最小值为 2.
2. shuffle : boolean型，可选参数，default=False，代表是否在切分数据前对数据进行洗牌操作
3. random_state : int型，可选参数，default=None，代表随机种子

根据帮助文档学习如何使用新的KFold函数，依此更改你的代码。

init类型错误

我也遇到了同样的问题，这样改一下就不会报错了

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold

将要预测的目标列

predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]

初始化算法类

alg = LinearRegression()
kf = KFold(n_splits=3, random_state=1,shuffle=False)

predictions = []
for train, test in kf.split(data["Survived"]):
train_predictors = data[predictors].iloc[train,:]
train_target = data["Survived"].iloc[train]
alg.fit(train_predictors, train_target)
test_predictions = alg.predict(data[predictors].iloc[test,:])
predictions.append(test_predictions)