DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the

参考链接：

https://blog.csdn.net/weixin_39223665/article/details/81268741

报错代码

from sklearn.svm import SVR

svm_reg = SVR(kernel="linear")
svm_reg.fit(housing_prepared, housing_labels)
housing_predictions = svm_reg.predict(housing_prepared)
svm_mse = mean_squared_error(housing_labels, housing_predictions)
svm_rmse = np.sqrt(svm_mse)
svm_rmse

报错内容：site-packages\sklearn\utils\validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
y = column_or_1d(y, warn=True)

解决方案

from sklearn.svm import SVR

svm_reg = SVR(kernel="linear")
svm_reg.fit(housing_prepared, pd.DataFrame(housing_labels).values.ravel())
housing_predictions = svm_reg.predict(housing_prepared)
svm_mse = mean_squared_error(housing_labels, housing_predictions)
svm_rmse = np.sqrt(svm_mse)
svm_rmse

###个人意见，不做处理好像也可以，不影响结果

前面是报错，然后我计算的均方误差的结果
后面是修改之后的计算均方误差的结果
理论上应该去看模型的参数来确定二者是不是一致的，但根据R语言的报错不影响运行的经验来看，应该是不影响的