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

目录

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

问题：

解决：

完整错误：

---

问题：

构建简单线性回归模型，特征只有一个的情况。使用一维numpy作为输入。

#
import numpy as np
from sklearn.linear_model import LinearRegression
X = np.array([1,2,3,4,5])
y = X*2+3
reg = LinearRegression().fit(X, y)
reg.score(X, y)
 
reg.coef_
 
reg.intercept_

解决：

使用reshape函数将一维转化为二维，OK了

import numpy as np
from sklearn.linear_model import LinearRegression
X = np.array([1,2,3,4,5])
y = X*2+3
reg = LinearRegression().fit(X.reshape(-1, 1), y)
reg.score(X.reshape(-1, 1), y)
 
reg.coef_
 
reg.intercept_

完整错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
 in 
      3 X = np.array([1,2,3,4,5])
      4 y = X*2+3
----> 5 reg = LinearRegression().fit(X, y)
      6 reg.score(X, y)
      7 

D:\anaconda\lib\site-packages\sklearn\linear_model\_base.py in fit(self, X, y, sample_weight)
    517 
    518         X, y = self._validate_data(X, y, accept_sparse=accept_sparse,
--> 519                                    y_numeric=True, multi_output=True)
    520 
    521         if sample_weight is not None:

D:\anaconda\lib\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
    431                 y = check_array(y, **check_y_params)
    432             else:
--> 433                 X, y = check_X_y(X, y, **check_params)
    434             out = X, y
    435 

D:\anaconda\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

D:\anaconda\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
    876                     ensure_min_samples=ensure_min_samples,
    877                     ensure_min_features=ensure_min_features,
--> 878                     estimator=estimator)
    879     if multi_output:
    880         y = check_array(y, accept_sparse='csr', force_all_finite=True,

D:\anaconda\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

D:\anaconda\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
    696                     "Reshape your data either using array.reshape(-1, 1) if "
    697                     "your data has a single feature or array.reshape(1, -1) "
--> 698                     "if it contains a single sample.".format(array))
    699 
    700         # make sure we actually converted to numeric:

ValueError: Expected 2D array, got 1D array instead:
array=[1 2 3 4 5].
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.