ValueError: Expected 2D array, got 1D array instead:

问题

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

Here is my data set:

Here is independent variable X:

Here is dependent variable Y:

Here is X_train

Here Is Y_train

This is error body:

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.

And this is My code:

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)

Thank you

回答1:

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)

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?

回答2:

If you look at documentation of LinearRegression of scikit-learn.

fit(X, y, sample_weight=None)

X : numpy array or sparse matrix of shape [n_samples,n_features]

predict(X)

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

As you can see X has 2 dimensions, where as, your x_train and x_test clearly have one. As suggested, add:

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

Before fitting and predicting the model.

回答3:

Use

y_pred = regressor.predict([[x_test]])

回答4:

This is the solution

regressor.predict([[x_test]])

And for polynomial regression:

regressor_2.predict(poly_reg.fit_transform([[x_test]]))

来源：https://stackoverflow.com/questions/51150153/valueerror-expected-2d-array-got-1d-array-instead

标签

python-3.x

scikit-learn

linear-regression