我正在尝试y_train_actual使用原始值计算来自我的 sci-kit 学习模型的预测的均方误差salaries。
问题：但是mean_squared_error(y_train_actual, salaries)，我得到了错误TypeError: unsupported operand type(s) for -: 'numpy.ndarray' and 'numpy.ndarray'。使用list(salaries)而不是salaries作为第二个参数会产生相同的错误。
随着mean_squared_error(y_train_actual, y_valid_actual)我得到错误Found array with dim 40663. Expected 244768
如何转换为正确的数组类型sklearn.netrucs.mean_squared_error()？
代码
from sklearn.metrics import mean_squared_error
y_train_actual = [ np.exp(float(row)) for row in y_train ]
print mean_squared_error(y_train_actual, salaries)
错误
TypeError Traceback (most recent call last)
<ipython-input-144-b6d4557ba9c5> in <module>()
3 y_valid_actual = [ np.exp(float(row)) for row in y_valid ]
4
----> 5 print mean_squared_error(y_train_actual, salaries)
6 print mean_squared_error(y_train_actual, y_valid_actual)
C:\Python27\lib\site-packages\sklearn\metrics\metrics.pyc in mean_squared_error(y_true, y_pred)
1462 """
1463 y_true, y_pred = check_arrays(y_true, y_pred)
-> 1464 return np.mean((y_pred - y_true) ** 2)
1465
1466
TypeError: unsupported operand type(s) for -: 'numpy.ndarray' and 'numpy.ndarray'
代码
y_train_actual = [ np.exp(float(row)) for row in y_train ]
y_valid_actual = [ np.exp(float(row)) for row in y_valid ]
print mean_squared_error(y_train_actual, y_valid_actual)
错误
ValueError Traceback (most recent call last)
<ipython-input-146-7fcd0367c6f1> in <module>()
4
5 #print mean_squared_error(y_train_actual, salaries)
----> 6 print mean_squared_error(y_train_actual, y_valid_actual)
C:\Python27\lib\site-packages\sklearn\metrics\metrics.pyc in mean_squared_error(y_true, y_pred)
1461
1462 """
-> 1463 y_true, y_pred = check_arrays(y_true, y_pred)
1464 return np.mean((y_pred - y_true) ** 2)
1465
C:\Python27\lib\site-packages\sklearn\utils\validation.pyc in check_arrays(*arrays, **options)
191 if size != n_samples:
192 raise ValueError("Found array with dim %d. Expected %d"
--> 193 % (size, n_samples))
194
195 if not allow_lists or hasattr(array, "shape"):
ValueError: Found array with dim 40663. Expected 244768
代码
print type(y_train)
print type(y_train_actual)
print type(salaries)
结果
<type 'list'>
<type 'list'>
<type 'tuple'>
打印 y_train[:10]
[10.126631103850338, 10.308952660644293, 10.308952660644293, 10.221941283654663, 10.126631103850338, 10.126631103850338, 11.225243392518447, 9.9987977323404529, 10.043249494911286, 11.350406535472453]
打印工资[:10]
('25000', '30000', '30000', '27500', '25000', '25000', '75000', '22000', '23000', '85000')
打印清单（工资）[:10]
['25000', '30000', '30000', '27500', '25000', '25000', '75000', '22000', '23000', '85000']
打印长度（y_train）
244768
打印镜头（工资）
244768