The loss squared_error is not supported for SGDRegressor

### Describe the bug

Hi I'm using SGDRegressor for OLS regression but encountered this error:

ValueError: The loss squared_error is not supported. 

### Steps/Code to Reproduce

from sklearn.linear_model import SGDRegressor
clf = SGDRegressor(loss="squared_error")
X = het_data[:, [1,3,4,5,7,8,9]]
Y = het_data[:, 6]
clf.fit(X, y)
# het_data is the prepared data

### Expected Results

No error 

### Actual Results

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-99-aec03d97b9d6> in <module>
----> 1 clf = SGDRegressor(loss="squared_error")
      2 '''
      3 1. average price: np.mean(temp['features']['Item_NCurrentPrice'])/100
      4 2. weighted price: temp['features']['Item_NCurrentPrice']@click_propensity[min(12, len(temp))]/100
      5 3. logged price: np.mean(np.log(temp['features']['Item_NCurrentPrice']))/100

~/opt/anaconda3/lib/python3.8/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

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_stochastic_gradient.py in __init__(self, loss, penalty, alpha, l1_ratio, fit_intercept, max_iter, tol, shuffle, verbose, epsilon, random_state, learning_rate, eta0, power_t, early_stopping, validation_fraction, n_iter_no_change, warm_start, average)
   1580                  power_t=0.25, early_stopping=False, validation_fraction=0.1,
   1581                  n_iter_no_change=5, warm_start=False, average=False):
-> 1582         super().__init__(
   1583             loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
   1584             fit_intercept=fit_intercept, max_iter=max_iter, tol=tol,

~/opt/anaconda3/lib/python3.8/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

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_stochastic_gradient.py in __init__(self, loss, penalty, alpha, l1_ratio, fit_intercept, max_iter, tol, shuffle, verbose, epsilon, random_state, learning_rate, eta0, power_t, early_stopping, validation_fraction, n_iter_no_change, warm_start, average)
   1127                  power_t=0.25, early_stopping=False, validation_fraction=0.1,
   1128                  n_iter_no_change=5, warm_start=False, average=False):
-> 1129         super().__init__(
   1130             loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
   1131             fit_intercept=fit_intercept, max_iter=max_iter, tol=tol,

~/opt/anaconda3/lib/python3.8/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

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_stochastic_gradient.py in __init__(self, loss, penalty, alpha, C, l1_ratio, fit_intercept, max_iter, tol, shuffle, verbose, epsilon, random_state, learning_rate, eta0, power_t, early_stopping, validation_fraction, n_iter_no_change, warm_start, average)
    100         # current tests expect init to do parameter validation
    101         # but we are not allowed to set attributes
--> 102         self._validate_params()
    103 
    104     def set_params(self, **kwargs):

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_stochastic_gradient.py in _validate_params(self, for_partial_fit)
    154 
    155         if self.loss not in self.loss_functions:
--> 156             raise ValueError("The loss %s is not supported. " % self.loss)
    157 
    158     def _get_loss_function(self, loss):

ValueError: The loss squared_error is not supported. 

### Versions

```shell
System:
    python: 3.8.8 (default, Apr 13 2021, 12:59:45)  [Clang 10.0.0 ]
executable: /Users/luyazhang/opt/anaconda3/bin/python
   machine: macOS-10.15.7-x86_64-i386-64bit

Python dependencies:
          pip: 21.2.4
   setuptools: 52.0.0.post20210125
      sklearn: 0.24.1
        numpy: 1.20.1
        scipy: 1.6.2
       Cython: 0.29.23
       pandas: 1.2.4
   matplotlib: 3.3.4
       joblib: 1.0.1
threadpoolctl: 2.1.0

Built with OpenMP: True
```

COMMENT:
You are using an old version of scikit-learn 0.24.1 while the latest version is 1.1.2. `"squared_error"` has been added in more recent versions. For old versions you can use `"squared_loss"`, but I'd advise to upgrade to a more recent version.

Labels: Bug, Needs Triage