python - SVR 模型 --> 特征缩放 - 预期的二维数组，取而代之的是一维数组

Question

我试图理解下面的代码有什么问题。我知道 Y 变量是 1D 数组，预计是 2D 数组，需要 reshape 结构，但该代码之前可以正常工作一个警告。

# Importing the libraries
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd

# Importing the dataset
  dataset = pd.read_csv('Position_Salaries.csv')
  X = dataset.iloc[:, 1:2].values
  y = dataset.iloc[:, 2].values



# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X = sc_X.fit_transform(X)
y = sc_y.fit_transform(y)

---

ValueError: Expected 2D array, got a 1D array instead:
array=[  45000.   50000.   60000.   80000.  110000.  150000.  200000.  300000.
  500000. 1000000.].
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

解决方法在报错信息中:

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.

由于您传递的是单个特征(而不是单个样本)，请尝试:

y = sc_y.fit_transform(y.reshape(-1, 1))