Redundant DataConversionWarning in BaseForest

### Describe the bug

A column vector is expected in `*Forest` even though the `y` is reshaped to be 2D:

```
        y = np.atleast_1d(y)
        if y.ndim == 2 and y.shape[1] == 1:
            warn(
                (
                    "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()."
                ),
                DataConversionWarning,
                stacklevel=2,
            )

        if y.ndim == 1:
            # reshape is necessary to preserve the data contiguity against vs
            # [:, np.newaxis] that does not.
            y = np.reshape(y, (-1, 1))
```

ref: https://github.com/scikit-learn/scikit-learn/blob/1d51c2eda47ba8db85f3d287ce2911986fe73d2b/sklearn/ensemble/_forest.py#L390-L405

### Steps/Code to Reproduce

```
from sklearn.ensemble import RandomForestClassifier

est = RandomForestClassifier()
est.fit(np.array([[0., 0.1], [1., 1.5]], y=np.array([0, 1]).reshape((-1, 1)))
```



### Expected Results

The warning seems redundant considering the array is transformed into a 2D array right afterwards anyways. I'm wondering if there is any reason to keep this warning here?

### Actual Results

```
E           sklearn.exceptions.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().

sktree/_lib/sklearn/ensemble/_forest.py:467: DataConversionWarning
```

### Versions

```shell
>>> import sklearn; sklearn.show_versions()

System:
    python: 3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:48:25)  [Clang 14.0.6 ]
executable: /Users/adam2392/miniforge3/envs/sktree/bin/python
   machine: macOS-13.5.2-arm64-arm-64bit

Python dependencies:
      sklearn: 1.3.0
          pip: 23.2.1
   setuptools: 65.7.0
        numpy: 1.25.2
        scipy: 1.10.1
       Cython: 0.29.36
       pandas: 2.0.3
   matplotlib: 3.7.2
       joblib: 1.3.2
threadpoolctl: 3.1.0

Built with OpenMP: True

threadpoolctl info:
       user_api: openmp
   internal_api: openmp
         prefix: libomp
       filepath: /Users/adam2392/miniforge3/envs/sktree/lib/python3.9/site-packages/sklearn/.dylibs/libomp.dylib
        version: None
    num_threads: 8

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /Users/adam2392/miniforge3/envs/sktree/lib/python3.9/site-packages/numpy/.dylibs/libopenblas64_.0.dylib
        version: 0.3.23.dev
threading_layer: pthreads
   architecture: armv8
    num_threads: 8

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /Users/adam2392/miniforge3/envs/sktree/lib/python3.9/site-packages/scipy/.dylibs/libopenblas.0.dylib
        version: 0.3.18
threading_layer: pthreads
   architecture: armv8
    num_threads: 8
```
```

COMMENT:
But our official API (as of now) is to always pass y for single output classification or regression as a 1d array.

If we start to accept 2d arrays for RFs, we would have to be consistent and do it everywhere in the library. I don't really see the value in that.

Labels: Bug, Needs Triage