错误时：TypeError: float() argument must be a string or a number, not 'method'
代码是：
predictors = ['Pclass','Sex','Age','SibSp','Parch','Fare','Embarked']
alg = LinearRegression()
kf = KFold(n_splits=3,random_state=1)#将891个样本平均切分成三分
predictions = []
for train,test in kf.split(titanic[predictors]): #train,test是原始数据集本身的索引,将数据分为训练集与测试集，并返回索引
#print(train)
#print(kf.get_n_splits())
train_predictiors = titanic[predictors].ix[train]
#print(train_predictiors)
train_target = titanic['Survived'].ix[train]
#print(train_target)
alg.fit(train_predictiors,train_target)
test_predictions = alg.predict(titanic[predictors].iloc[test,:])
predictions.append(test_predictions)
代码是：
predictors = ['Pclass','Sex','Age','SibSp','Parch','Fare','Embarked']
alg = LinearRegression()
kf = KFold(n_splits=3,random_state=1)#将891个样本平均切分成三分
predictions = []
for train,test in kf.split(titanic[predictors]): #train,test是原始数据集本身的索引,将数据分为训练集与测试集，并返回索引
#print(train)
#print(kf.get_n_splits())
train_predictiors = titanic[predictors].ix[train]
#print(train_predictiors)
train_target = titanic['Survived'].ix[train]
#print(train_target)
alg.fit(train_predictiors,train_target)
test_predictions = alg.predict(titanic[predictors].iloc[test,:])
predictions.append(test_predictions)
