ValueError:预期的2D数组，而是1D数组: [英] ValueError: Expected 2D array, got 1D array instead:

问题描述

在练习简单线性回归模型时，我遇到了这个错误，我认为我的数据集有问题.

While practicing Simple Linear Regression Model I got this error, I think there is something wrong with my data set.

这是我的数据集:

此处是自变量X:

此处是因变量Y:

这是X_train

这是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

推荐答案

您需要同时提供

You need to give both the fit and predict methods 2D arrays. Your x_train, y_train and x_test are currently only 1D. What is suggested by the console should work:

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 .过去已经回答过有关 reshape 的问题，例如，这应该回答 reshape(-1,1)的含义:

This uses numpy's reshape. Questions about reshape have been answered in the past, this for example should answer what reshape(-1,1) means: What does -1 mean in numpy reshape?

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