GrideSearchCV() has Issue with LSSVM () classification

### Describe the workflow you want to enable

Hi, 
I tried to use `GrideSearchCV()` with `LSSVM()` but could not do that , please could you help ?
 The code : 
The code which i used is from Github romolo code.
[https://github.com/RomuloDrumond/LSSVM](https://www.researchgate.net/deref/https%3A%2F%2Fgithub.com%2FRomuloDrumond%2FLSSVM?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6Il9kaXJlY3QiLCJwYWdlIjoiX2RpcmVjdCJ9fQ)

```python
lssvc = LSSVC(gamma=1, kernel='rbf', sigma=.5) # Class instantiation
lssvc.fit(X_tr_norm, y_train) # Fitting the model
y_pred = lssvc.predict(X_ts_norm) # Making predictions with the trained model
acc = accuracy_score(dummie2multilabel(y_test), dummie2multilabel(y_pred)) # Calculate Accuracy
print('acc_test = ', acc, '\n')
```

Then tried to do `GridesearchCV()` :

```python
parameters = {'kernel':('rbf'),
'gamma':[0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0],
'sigma':[0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0]}
lssvm = LSSVC()
LSSVC= GridSearchCV(lssvm, parameters)
lssvc.fit(X_tr_norm, y_train) # Fitting the model
y_pred = lssvc.predict(X_ts_norm) # Making predictions with the trained model
acc = accuracy_score(dummie2multilabel(y_test), dummie2multilabel(y_pred)) # Calculate Accuracy
print('acc_test = ', acc, '\n')
```

There are other implementaion for LSSVM but non of them worked with GrideSearchCV() since not in the original sklearn ?

### Describe your proposed solution

Adding LSSVM to GridesearchCV() 

### Describe alternatives you've considered, if relevant

No alternatives 

### Additional context

_No response_

COMMENT:
```pytb
TypeError                                 Traceback (most recent call last)
Cell In[3], line 17
     15 lssvcs= GridSearchCV(lssvm, parameters)
     16 #--------------------------------------------------------------------------------------------------
---> 17 lssvcs.fit(X_tr_norm, y_train) # Fitting the model
     18 y_pred = lssvc.predict(X_ts_norm) # Making predictions with the trained model
     19 acc = accuracy_score(dummie2multilabel(y_test), dummie2multilabel(y_pred)) # Calculate Accuracy

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1151, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1144     estimator._validate_params()
   1146 with config_context(
   1147     skip_parameter_validation=(
   1148         prefer_skip_nested_validation or global_skip_validation
   1149     )
   1150 ):
-> 1151     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:800, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    798     scorers = self.scoring
    799 elif self.scoring is None or isinstance(self.scoring, str):
--> 800     scorers = check_scoring(self.estimator, self.scoring)
    801 else:
    802     scorers = _check_multimetric_scoring(self.estimator, self.scoring)

File ~\anaconda3\Lib\site-packages\sklearn\utils\_param_validation.py:211, in validate_params.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    205 try:
    206     with config_context(
    207         skip_parameter_validation=(
    208             prefer_skip_nested_validation or global_skip_validation
    209         )
    210     ):
--> 211         return func(*args, **kwargs)
    212 except InvalidParameterError as e:
    213     # When the function is just a wrapper around an estimator, we allow
    214     # the function to delegate validation to the estimator, but we replace
    215     # the name of the estimator by the name of the function in the error
    216     # message to avoid confusion.
    217     msg = re.sub(
    218         r"parameter of \w+ must be",
    219         f"parameter of {func.__qualname__} must be",
    220         str(e),
    221     )

File ~\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py:953, in check_scoring(estimator, scoring, allow_none)
    951     return None
    952 else:
--> 953     raise TypeError(
    954         "If no scoring is specified, the estimator passed should "
    955         "have a 'score' method. The estimator %r does not." % estimator
    956     )

TypeError: If no scoring is specified, the estimator passed should have a 'score' method. The estimator <lssvm.LSSVC.LSSVC object at 0x000002642D821ED0> does not.
```

Labels: New Feature, Needs Triage