AttributeError: 'OneHotEncoder' object has no attribute '_drop_idx_after_grouping'

### Describe the bug

Context:
I am encountering an issue while using the OneHotEncoder class from scikit-learn. When trying to transform my data using the transform method, I receive the following error:
AttributeError: 'OneHotEncoder' object has no attribute '_drop_idx_after_grouping'
Steps to Reproduce:

Import the necessary modules and classes from scikit-learn.
Create an instance of the OneHotEncoder class.
Call the transform method on the OneHotEncoder object with the input data.
Expected Behavior:
I expected the OneHotEncoder class to transform the input data into one-hot encoded format without any errors.

Actual Behavior:
Instead, the AttributeError is raised, indicating that the '_drop_idx_after_grouping' attribute does not exist.
Additional Information:

I have verified that I am using the latest version of scikit-learn.
I have not directly referenced or used the '_drop_idx_after_grouping' attribute in my code, so I suspect it may be an internal issue within the scikit-learn library.

Environment:
```
Python version: 3.12.0
scikit-learn version:  1.4.1.post1
Operating system: Jupyter Notebook for coding part and PyCharm for web development
```

### Steps/Code to Reproduce

```python
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder

trf = ColumnTransformer(
    transformers=[
        ('trf', OneHotEncoder(sparse_output=False), ['batting_team', 'bowling_team', 'city'])
    ],
    remainder='passthrough'
)
```

### Expected Results

While developing the project for web I was expecting the winning probability of a team, but I got Attribute error.

### Actual Results

```pytb
AttributeError: 'OneHotEncoder' object has no attribute '_drop_idx_after_grouping'
Traceback:
File "C:\Users\Tnluser.PG02YSJ5\IPL_win_predictor\pythonProject2\.venv\Lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 542, in _run_script
    exec(code, module.__dict__)
File "C:\Users\Tnluser.PG02YSJ5\IPL_win_predictor\pythonProject2\app.py", line 55, in <module>
    result = pipe.predict_proba(input_df)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Tnluser.PG02YSJ5\IPL_win_predictor\pythonProject2\.venv\Lib\site-packages\sklearn\pipeline.py", line 722, in predict_proba
    Xt = transform.transform(Xt)
         ^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Tnluser.PG02YSJ5\IPL_win_predictor\pythonProject2\.venv\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped
    data_to_wrap = f(self, X, *args, **kwargs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Tnluser.PG02YSJ5\IPL_win_predictor\pythonProject2\.venv\Lib\site-packages\sklearn\compose\_column_transformer.py", line 1014, in transform
    Xs = self._call_func_on_transformers(
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Tnluser.PG02YSJ5\IPL_win_predictor\pythonProject2\.venv\Lib\site-packages\sklearn\compose\_column_transformer.py", line 823, in _call_func_on_transformers
    return Parallel(n_jobs=self.n_jobs)(jobs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Tnluser.PG02YSJ5\IPL_win_predictor\pythonProject2\.venv\Lib\site-packages\sklearn\utils\parallel.py", line 67, in __call__
    return super().__call__(iterable_with_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Tnluser.PG02YSJ5\IPL_win_predictor\pythonProject2\.venv\Lib\site-packages\joblib\parallel.py", line 1863, in __call__
    return output if self.return_generator else list(output)
                                                ^^^^^^^^^^^^
File "C:\Users\Tnluser.PG02YSJ5\IPL_win_predictor\pythonProject2\.venv\Lib\site-packages\joblib\parallel.py", line 1792, in _get_sequential_output
    res = func(*args, **kwargs)
          ^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Tnluser.PG02YSJ5\IPL_win_predictor\pythonProject2\.venv\Lib\site-packages\sklearn\utils\parallel.py", line 129, in __call__
    return self.function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Tnluser.PG02YSJ5\IPL_win_predictor\pythonProject2\.venv\Lib\site-packages\sklearn\pipeline.py", line 1283, in _transform_one
    res = transformer.transform(X, **params.transform)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Tnluser.PG02YSJ5\IPL_win_predictor\pythonProject2\.venv\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped
    data_to_wrap = f(self, X, *args, **kwargs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Tnluser.PG02YSJ5\IPL_win_predictor\pythonProject2\.venv\Lib\site-packages\sklearn\preprocessing\_encoders.py", line 1032, in transform
    if self._drop_idx_after_grouping is not None:
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
```

### Versions

```shell
System:
    python: 3.10.9 | packaged by Anaconda, Inc. | (main, Mar  1 2023, 18:18:15) [MSC v.1916 64 bit (AMD64)]
executable: C:\Users\Tnluser.PG02YSJ5\anaconda3\python.exe
   machine: Windows-10-10.0.19045-SP0

Python dependencies:
      sklearn: 1.4.1.post1
          pip: 22.3.1
   setuptools: 65.6.3
        numpy: 1.23.5
        scipy: 1.10.0
       Cython: 3.0.2
       pandas: 1.5.3
   matplotlib: 3.7.0
       joblib: 1.3.2
threadpoolctl: 2.2.0

Built with OpenMP: True

threadpoolctl info:
       filepath: C:\Users\Tnluser.PG02YSJ5\anaconda3\Lib\site-packages\sklearn\.libs\vcomp140.dll
         prefix: vcomp
       user_api: openmp
   internal_api: openmp
        version: None
    num_threads: 8

       filepath: C:\Users\Tnluser.PG02YSJ5\anaconda3\Library\bin\mkl_rt.1.dll
         prefix: mkl_rt
       user_api: blas
   internal_api: mkl
        version: 2021.4-Product
    num_threads: 4
threading_layer: intel
```

COMMENT:
Since there is a `pkl` file, I don't want to unpickle it for security reason.
What I can infer from here is that the `OneHotEncoder` was pickle with another version than 1.4.1.post1 where the attribute did not exist.

You need to install the exact same library version than when persisting the model.

You can have a look at this documentation: https://scikit-learn.org/stable/model_persistence.html#model-persistence

Labels: Bug