Automatic bandwidth calculation valid only for normalized data

### Describe the bug

`sklearn.neighbors.KernelDensity` supports automatic (optimal) bandwidth calculation via `bandwidth = 'silverman'` and `bandwidth = 'scott'`. The algorithm computes the appropriate observation-weighted bandwidth factors (proportional to nobs^0.2) but does not adjust for the standard deviation or interquartile range of the dataset. Roughly, the algorithm should scale the dataset's standard error by the algorithmic bandwidth factors.

See, e.g., [Wikipedia](https://en.wikipedia.org/wiki/Kernel_density_estimation#A_rule-of-thumb_bandwidth_estimator). The implementation in `scipy.stats._kde` is correct.

### Steps/Code to Reproduce
```python
import matplotlib.pyplot as plot
import numpy as np
from sklearn.neighbors import KernelDensity
from scipy.stats import gaussian_kde

data = np.random.normal( scale = 0.01, size = 100 )

#
# 1. sklearn (auto)
#
kd_sklearn_auto = KernelDensity( kernel = 'gaussian', bandwidth = 'silverman' )
kd_sklearn_auto.fit( np.reshape( data, ( -1, 1 ) ) )

#
# 2. sklearn (manual)
#
kd_sklearn_manual = KernelDensity( kernel = 'gaussian', bandwidth = 0.9 * np.std( data ) / len( data ) ** ( 1 / 5 ) )
kd_sklearn_manual.fit( np.reshape( data, ( -1, 1 ) ) )

#
# 3. scipy
#
kd_scipy = gaussian_kde( data, bw_method = 'silverman' )

#
# 4. show the difference
#
xs = np.arange( start = -0.05, stop = 0.05, step = 1e-4 )
plot.plot( xs, np.exp( kd_sklearn_auto.score_samples( np.reshape( xs, ( -1, 1 ) ) ) ), label = 'KDE SKLearn (auto)' )
plot.plot( xs, np.exp( kd_sklearn_manual.score_samples( np.reshape( xs, ( -1, 1 ) ) ) ), label = 'KDE SKLearn (manual)' )
plot.plot( xs, kd_scipy.pdf( xs ), label = 'KDE SciPy' )
plot.hist( data, label = 'Data' )
plot.legend()
plot.show()
```

### Expected Results

Automatic SKLearn bandwidth curve should approximately match SciPy bandwidth curve, roughly the shape of the underlying data histogram.

### Actual Results

Automatic SKLearn bandwidth curve generates a flat PDF.

### Versions

```shell
System:
    python: 3.9.5 (default, Nov 23 2021, 15:27:38)  [GCC 9.3.0]
executable: /local_disk0/.ephemeral_nfs/envs/pythonEnv-67c47d19-3f15-49a2-ab8f-bcf25a2bc29f/bin/python
   machine: Linux-5.15.0-1038-azure-x86_64-with-glibc2.31

Python dependencies:
      sklearn: 1.2.2
          pip: 21.2.4
   setuptools: 58.0.4
        numpy: 1.20.3
        scipy: 1.7.1
       Cython: 0.29.24
       pandas: 1.3.4
   matplotlib: 3.4.3
       joblib: 1.2.0
threadpoolctl: 2.2.0

Built with OpenMP: True

threadpoolctl info:
       filepath: /databricks/python3/lib/python3.9/site-packages/numpy.libs/libopenblasp-r0-5bebc122.3.13.dev.so
         prefix: libopenblas
       user_api: blas
   internal_api: openblas
        version: 0.3.13.dev
    num_threads: 6
threading_layer: pthreads
   architecture: Haswell

       filepath: /local_disk0/.ephemeral_nfs/envs/pythonEnv-67c47d19-3f15-49a2-ab8f-bcf25a2bc29f/lib/python3.9/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
         prefix: libgomp
       user_api: openmp
   internal_api: openmp
        version: None
    num_threads: 6

       filepath: /databricks/python3/lib/python3.9/site-packages/scipy.libs/libopenblasp-r0-085ca80a.3.9.so
         prefix: libopenblas
       user_api: blas
   internal_api: openblas
        version: 0.3.9
    num_threads: 6
threading_layer: pthreads
   architecture: Haswell
```

COMMENT:
Note that the 2 approaches are not necessarily mutually exclusive. In the future we could decide to implement the variance based approach as well if someone can highlight a real-life case where approach 1 would perform significantly better than approach two.

Labels: Bug