Unexpected behavior of sklearn.feature_selection.mutual_info_regression if copy=False

### Describe the bug

The parameter `copy` of the function `mutual_info_regression` is described as follows https://github.com/scikit-learn/scikit-learn/blob/d1d1596fac19d688a637690134d71fc460f5f0dd/sklearn/feature_selection/_mutual_info.py#L381-L383

I read it as both `X` and `y` should be modified if `copy=False` and `X` has continuous features. However, `y`  is always copied. I think the lines 
https://github.com/scikit-learn/scikit-learn/blob/d1d1596fac19d688a637690134d71fc460f5f0dd/sklearn/feature_selection/_mutual_info.py#L309-L310 should be 
```python
    if not discrete_target:
        y = y.astype(np.float64, copy=copy)
        y = scale(y, with_mean=False, copy=False)
```
Similarly to the the treatment of `X` https://github.com/scikit-learn/scikit-learn/blob/d1d1596fac19d688a637690134d71fc460f5f0dd/sklearn/feature_selection/_mutual_info.py#L295-L299

### Steps/Code to Reproduce

```python
import numpy as np
from sklearn.feature_selection import mutual_info_regression
n_samples_, n_feats = 30, 2
X = np.random.randn(n_samples_, n_feats)
y = np.random.randn(n_samples_, )
y_copy = y.copy()
X_copy = X.copy()
mutual_info_regression(X, y, copy=False)
print(np.allclose(y, y_copy), np.allclose(X, X_copy))
```

### Expected Results

The result should be
```
False, False
```
since both `X` and `y` should be modified in place by the function `mutual_info_regression`. 



### Actual Results

```
True, False
```

### Versions

```shell
System:
    python: 3.11.7 (main, Dec  5 2023, 19:13:35) [GCC 10.2.1 20210110]
executable: /usr/local/bin/python
   machine: Linux-5.15.146.1-microsoft-standard-WSL2-x86_64-with-glibc2.31

Python dependencies:
      sklearn: 1.3.0
          pip: 23.3.1
   setuptools: 69.0.2
        numpy: 1.23.2
        scipy: 1.12.0
       Cython: 3.0.9
       pandas: 2.1.4
   matplotlib: 3.8.2
       joblib: 1.3.2
threadpoolctl: 3.2.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 12
         prefix: libopenblas
       filepath: /home/user/.local/lib/python3.11/site-packages/numpy.libs/libopenblas64_p-r0-742d56dc.3.20.so
        version: 0.3.20
threading_layer: pthreads
   architecture: Prescott

       user_api: blas
   internal_api: openblas
    num_threads: 12
         prefix: libopenblas
       filepath: /home/user/.local/lib/python3.11/site-packages/scipy.libs/libopenblasp-r0-23e5df77.3.21.dev.so
        version: 0.3.21.dev
threading_layer: pthreads
   architecture: Prescott

       user_api: openmp
   internal_api: openmp
    num_threads: 12
         prefix: libgomp
       filepath: /home/user/.local/lib/python3.11/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
        version: None
```

COMMENT:
Thanks for the response!

An example code would be something like 
```python
# We rescale X and y in the sklearn method computing I(x_i; y) for all i:
mutual_info = mutual_info_regression(X, y, copy=False)
# We re-use the rescaled X and y in our method computing I(x_i; y| x_j) 
# for all i and j , where we do not modify X or y
conditional_mutual_info = conditional_mutual_info(X, y)
```
Note that the mutual_info methods rescale and add noise to `X` and `y`, which I wouldn't want to do several times. Also I want to use the same `X` and `y` for all the computations/estimations. 

But if I understood you correctly using `copy=False` doesn't give me much guarantees on what will happen to `X` after the call to this method and I shouldn't rely on this functionality? If so then I may need to look for a different solution anyway.

On another note, I think importing the private `sklearn` method wouldn't work in the long run for our plug-in.

Labels: Documentation, help wanted