Diabetes data should match the original source

### Describe the bug

When `load_diabetes` is called with `scaled=False`, the `s5` attribute has some values with insufficient precision:
All values should stay equal when rounded to 4 decimals, but 11 of them don't.

This is caused by the fact that the unpacked `sklearn/datasets/data/diabetes_data_raw.csv.gz` has some numeric differences to the original data.
E.g. entry nr. 147 contains `4.803999999999999` here (in line 147), and `4.804` in the original (line 148 because of header).

The following example shows different behavior when the data source is toggled with `use_internal`.

I need the correct data because I have code that tries to autodetect the precision – which currently cannot detect the correct precision of `s5`.

### Steps/Code to Reproduce

```python
from sklearn.datasets import load_diabetes
import requests
from io import StringIO
import pandas as pd

use_internal = True
if use_internal:
    diabetes = load_diabetes(as_frame=True, scaled=False)
    s5 = diabetes.frame['s5']
else:
    # diabetes.DESCR names https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html
    # as source URL.
    # There the following orig_url is linked as the original data set.
    orig_url = 'https://www4.stat.ncsu.edu/~boos/var.select/diabetes.tab.txt'
    response = requests.get(orig_url)
    response.raise_for_status()
    data = StringIO(response.text)
    diabetes = pd.read_csv(data, sep='\t')
    s5 = diabetes['S5']

rounded = s5.round(4)
pd.set_option('display.precision', 16)
diff = s5[s5 != rounded] 
print(diff)
assert(diff.empty)

```

### Expected Results

`Series([], Name: S2, dtype: float64)`

and no assertion.

(As with `use_internal = False`)


### Actual Results

```
146    4.8039999999999994
239    5.3660000000000005
265    4.8039999999999994
303    5.4510000000000005
313    5.2470000000000008
324    5.3660000000000005
359    4.8039999999999994
364    4.8039999999999994
410    5.3660000000000005
415    4.8039999999999994
428    5.3660000000000005
Name: s5, dtype: float64
```
and the `diff.empty` assertion.

(In contrast to `use_internal = False`)

### Versions

```shell
System:
    python: 3.12.3 (main, Jun 18 2025, 17:59:45) [GCC 13.3.0]
executable: /home/robert/projects/lcm-stp/fuchs23/env312/bin/python3
   machine: Linux-6.14.0-27-generic-x86_64-with-glibc2.39

Python dependencies:
      sklearn: 1.5.1
          pip: None
   setuptools: 75.8.0
        numpy: 2.1.1
        scipy: 1.14.1
       Cython: None
       pandas: 2.2.2
   matplotlib: 3.9.2
       joblib: 1.4.2
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 32
         prefix: libscipy_openblas
       filepath: /home/robert/projects/lcm-stp/fuchs23/env312/lib/python3.12/site-packages/numpy.libs/libscipy_openblas64_-ff651d7f.so
        version: 0.3.27
threading_layer: pthreads
   architecture: SkylakeX

       user_api: blas
   internal_api: openblas
    num_threads: 32
         prefix: libscipy_openblas
       filepath: /home/robert/projects/lcm-stp/fuchs23/env312/lib/python3.12/site-packages/scipy.libs/libscipy_openblas-c128ec02.so
        version: 0.3.27.dev
threading_layer: pthreads
   architecture: Cooperlake

       user_api: openmp
   internal_api: openmp
    num_threads: 32
         prefix: libgomp
       filepath: /home/robert/projects/lcm-stp/fuchs23/env312/lib/python3.12/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
        version: None
```

COMMENT:
1. The fix involved uncompressing diabetes_data_raw.csv.gz, fixing the eleven lines listed above under "Actual Results" to match the original values, and then recompressing the file. I now created pull request #31977 for this.
At the moment, I cannot think of any potential unintended consequences. Wouldn't any behavior change result from "chasing exact decimals"?
2. This is for the same reason the dataset is embedded in the first place.

Well, me and my code noticed. 🤷

Labels: module:datasets