Numpy Array Error when Training MultioutputClassifer with LogisticRegressionCV with classes underrepresented

### Describe the bug

When I train the MultioutputClassifer with LogisticRegressionCV with classes underrepresented, I get the following numpy error.
I think this is connected to the issue #28178 and #26401.

### Steps/Code to Reproduce

```python
import sklearn
print(sklearn.__version__)
from sklearn.linear_model import LogisticRegressionCV
from sklearn.multioutput import MultiOutputClassifier
import numpy as np


n, m = 20, 5
model = MultiOutputClassifier(LogisticRegressionCV())
X = np.random.randn(n, m)
y = np.concatenate([[np.random.randint(0, 2, n),
                     np.random.randint(0, 5, n)]], axis=0).T
y[-3:, 0] = [3, 4, 5]
model.fit(X, y)
```

### Expected Results

1.6.1

### Actual Results

1.6.1

```pytb
.venv/lib/python3.12/site-packages/sklearn/model_selection/_split.py:805: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.
  warnings.warn(
Traceback (most recent call last):
  File "error_skitlearn.py", line 14, in <module>
    model.fit(X, y)
  File ".venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 543, in fit
    super().fit(X, Y, sample_weight=sample_weight, **fit_params)
  File ".venv/lib/python3.12/site-packages/sklearn/base.py", line 1389, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File ".venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 274, in fit
    self.estimators_ = Parallel(n_jobs=self.n_jobs)(
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File ".venv/lib/python3.12/site-packages/sklearn/utils/parallel.py", line 77, in __call__
    return super().__call__(iterable_with_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File ".venv/lib/python3.12/site-packages/joblib/parallel.py", line 1918, in __call__
    return output if self.return_generator else list(output)
                                                ^^^^^^^^^^^^
  File ".venv/lib/python3.12/site-packages/joblib/parallel.py", line 1847, in _get_sequential_output
    res = func(*args, **kwargs)
          ^^^^^^^^^^^^^^^^^^^^^
  File ".venv/lib/python3.12/site-packages/sklearn/utils/parallel.py", line 139, in __call__
    return self.function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File ".venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 63, in _fit_estimator
    estimator.fit(X, y, **fit_params)
  File ".venv/lib/python3.12/site-packages/sklearn/base.py", line 1389, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File ".venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py", line 2038, in fit
    coefs_paths = np.reshape(
                  ^^^^^^^^^^^
  File ".venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py", line 324, in reshape
    return _wrapfunc(a, 'reshape', shape, order=order)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File ".venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py", line 54, in _wrapfunc
    return _wrapit(obj, method, *args, **kwds)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File ".venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py", line 42, in _wrapit
    conv = _array_converter(obj)
           ^^^^^^^^^^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
```

### Versions

```shell
System:
    python: 3.12.3 (main, Jan 17 2025, 18:03:48) [GCC 13.3.0]
executable: .venv/bin/python
   machine: Linux-6.8.0-52-generic-x86_64-with-glibc2.39

Python dependencies:
      sklearn: 1.6.1
          pip: 25.0.1
   setuptools: 75.8.0
        numpy: 2.2.3
        scipy: 1.15.1
       Cython: None
       pandas: 2.2.3
   matplotlib: 3.10.0
       joblib: 1.4.2
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 8
         prefix: libscipy_openblas
       filepath: .venv/lib/python3.12/site-packages/numpy.libs/libscipy_openblas64_-6bb31eeb.so
        version: 0.3.28
threading_layer: pthreads
   architecture: Haswell

       user_api: blas
   internal_api: openblas
    num_threads: 8
         prefix: libscipy_openblas
       filepath: .venv/lib/python3.12/site-packages/scipy.libs/libscipy_openblas-68440149.so
        version: 0.3.28
threading_layer: pthreads
   architecture: Haswell

       user_api: openmp
   internal_api: openmp
    num_threads: 8
         prefix: libgomp
       filepath: .venv/lib/python3.12/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
        version: None
```

COMMENT:
Hello @ogrisel and everyone. I'm working on this issue, and I'm trying to understand it the best way so I can fix it.

Do you think that increasing the dataset by repeating the data in each class until the class with the minimum number of samples reaches the number of splits is a bad idea?

Example:

old_dataset
[[ 0.12, -0.43],
 [ 1.23,  0.56],
 [-0.34,  0.78],
 [ 0.90, -1.56],
 [-0.56,  0.34]]

old_classes
[0, 1, 0, 1, 3]        --> Class 3 only has 1 sample ( Let's say we set the **n_splits** to **2** )

_* Some code to repeat the the dataset and classes *_

new_dataset
[[ 0.12, -0.43],
 [ 1.23,  0.56],
 [-0.34,  0.78],
 [ 0.90, -1.56],
 [-0.56,  0.34],
 [ 0.12, -0.43],
 [ 1.23,  0.56],
 [-0.34,  0.78],
 [ 0.90, -1.56],
 [-0.56,  0.34]]

new_classes
[0, 1, 0, 1, 3, 0, 1, 0, 1, 3]        --> Now, the number of samples in every class is **greater than or equal to n_splits**.

Even though it might lead to some overfitting, it is still better than getting a ValueError, right?
Are there any other tips someone can give me to help me fix this bug?

Any thoughts or tips will be helpful. 
I will keep working on this.
Thank you a lot!

Labels: Bug, Needs Investigation