ValueError：预期的2D数组，改为获得1D数组：

Question

在练习简单线性回归模型时我得到了这个错误，我认为我的数据集有问题。

Here is my data set:

Here is independent variable X:

Here is dependent variable Y:

Here is X_train

Here Is Y_train

这是错误正文：

ValueError: Expected 2D array, got 1D array instead:
array=[ 7.   8.4 10.1  6.5  6.9  7.9  5.8  7.4  9.3 10.3  7.3  8.1].
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.

这是我的代码：

import pandas as pd
import matplotlib as pt

#import data set

dataset = pd.read_csv('Sample-data-sets-for-linear-regression1.csv')
x = dataset.iloc[:, 1].values
y = dataset.iloc[:, 2].values

#Spliting the dataset into Training set and Test Set
from sklearn.cross_validation import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= 0.2, random_state=0)

#linnear Regression

from sklearn.linear_model import LinearRegression

regressor = LinearRegression()
regressor.fit(x_train,y_train)

y_pred = regressor.predict(x_test)

谢谢

Answer 1

您需要同时给出fit和predict方法2D数组。你的x_train，y_train和x_test目前只有1D。控制台建议的应该是：

x_train= x_train.reshape(-1, 1)
y_train= y_train.reshape(-1, 1)
x_test = x_test.reshape(-1, 1)

这使用了numpy的reshape。关于reshape的问题在过去已被回答，例如，这应该回答reshape(-1,1)的意思：What does -1 mean in numpy reshape?

Answer 2

如果你看看LinearRegression of scikit-learn的文档。

适合（X，y，sample_weight =无）

X：numpy数组或形状稀疏矩阵[n_samples，n_features]

预测（X）

X：{array-like，sparse matrix}，shape =（n_samples，n_features）

正如你所看到的X有两个维度，你的x_train和x_test显然有一个维度。如建议的那样，添加：

x_train = x_train.reshape(-1, 1)
x_test = x_test.reshape(-1, 1)

在拟合和预测模型之前。

Answer 3

这是你的答案。 使用：y_pred = regressor.predict([[x_test]])

我会帮你的。