为什么sklearn在train_test_split中给我一个值错误

Question

ValueError:期望的2D数组，得到的是1D数组;数组=[712。3)。重塑您的数据或使用数组。如果您的数据有单个特征或数组，则重塑(- 1,1)。如果包含单个样本，则重塑(1，-1)。

```
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
import seaborn as sb
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
df=sb.load_dataset('titanic')
df2=df[['survived','pclass','age','parch']]
df3=df2.fillna(df2.mean())
x=df3.drop('survived',axis=1)
y=df3['survived'] 
x_train,y_train,x_test,y_test=train_test_split(x,y,test_size=0.2, random_state=51)
print('x_train',x_train.shape)
sc=StandardScaler()
sc.fit(x_train.shape)
x_train=x_train.reshape(-1,1)

x_train_sc=sc.transform(x_train)
x_test_sc=sc.transform(x_test)
print(x_train_sc)`
```
```
`I would really appreciate if could fid me a solution
I have applied train_test_split to x & y variables and also transformed it into the x_train variabel. I was trying to print x_train. But it showed me an error
`
```
raise ValueError(
ValueError: Expected 2D array, got 1D array instead:
array=[712.   3.].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
```
`

Answer 1

你应该给你的StandardScaler你的X_train，而不是你的X_train的形状:)

sc=StandardScaler()
sc.fit(x_train)
x_train_sc=sc.transform(x_train)
x_test_sc=sc.transform(x_test)

如果您想在-1/1范围内规范化数据，最好使用MinMaxScaler:

from sklearn.preprocessing import MinMaxScaler
...
sc = MinMaxScaler(feature_range=(-1, 1)).fit(X_train)
x_train_sc=sc.transform(x_train)
x_test_sc=sc.transform(x_test)