Standard Deviation with GPR always between 0 and 1

### Describe the bug

I have been trying to fit a gpr interpolation to a set of data, but I keep finding that the standard deviation is always between 0 and 1. I have tried a 1-dimensional example which uses a sin graph and that produces std with a much greater range than my 16 variable problem, whose y values can range between -300 to 50.

Is this down to the kernel I have selected or is there something else that I can do to rectify this issue? This is what I am using currently

`

`

Thanks

### Steps/Code to Reproduce

```python
length_scale_param=1.9
length_scale_bounds_param=(1e-02, 100.0)
nu_param=2.5
matern=Matern(length_scale=length_scale_param,
              length_scale_bounds=length_scale_bounds_param,nu=nu_param)
kernel = matern + WhiteKernel()

gpr = Gpr(
        kernel=matern,#RBF(length_scale_bounds=(1.0e-10, 1.0e1)),
        n_restarts_optimizer=100,
    )
```

### Expected Results

I would expect the standard deviation to go beyond 1 at some point.

### Actual Results

```
X =  [[0.0999999557809357, 1.3299999629859667, 0.09999855094431032, 1.3299998916392302, 0.09999995283350248, 0.5000004336166886, -0.09999987344949994, 0.5000000291128839, 0.09999968571976785, 1.3299999913612839, -0.09999893694808444, 1.329999992898639, -0.09999962275499284, 1.3299999492423913, 0.09999936606708786, 1.329999994924743, -14.999999994821529]]
GPR(X) =  (array([0.45100584]), array([0.97051732]))


X =  [[-0.09999966505014607, 1.329999880690619, 0.09999945465135117, 0.5000000304384626, -0.09999976824132358, 0.5000000804875099, -0.09999761679762588, 0.5000017157059897, -0.09999988188916228, 1.3299997993502248, 0.09999882495398946, 0.5000000116867408, -0.09999975104800556, 1.3299998491078646, -0.09999969367805404, 0.5000005169037308, 12.074950768759113]]
GPR(X) =  (array([1.00625317]), array([0.90879639]))


X =  [[0.09999998342808601, 1.3299997973086852, 0.0999996454453132, 1.3299999869001065, 0.09999947501217771, 0.500000020285609, -0.09999897589865389, 0.5000000514458072, 0.09999995264500351, 1.3299999654153423, -0.09999339745129729, 1.3299998725882516, -0.09999982703721108, 1.3299999128563844, 0.09999642406913826, 1.329999979129611, -14.99999983040047]]
GPR(X) =  (array([0.45947313]), array([0.97383824]))


X =  [[0.001683069275882061, 1.3299746463161866, 0.09878254312295874, 0.5000068324919896, 0.09024987991077812, 0.5000037111327654, -0.09936594942273903, 1.3286947595217389, -0.09975200106275527, 0.5234780374848061, 0.09963871092667227, 0.500003120555903, -0.09943179096916806, 1.24136428260003, -0.09980879710138217, 0.5001128869054688, 11.952499482395757]]
GPR(X) =  (array([1.06140648]), array([0.9090287]))


X =  [[0.06393671346755857, 1.3299925070746588, -0.09996204776875718, 0.5000143189776588, 0.07305483009752621, 1.329386194855479, -0.09919264067152046, 0.5000516888985612, -0.0999887981111088, 0.5000153171131948, 0.09995790588789868, 0.5000259206116895, -0.09979274075550092, 1.3299714770859394, 0.09932147573629936, 1.3298664732583132, 11.808032457981113]]
GPR(X) =  (array([1.22841009]), array([0.96018541]))


X =  [[-0.09997303005814892, 0.769143784931105, -0.0997087964830409, 0.5000197115782452, 0.09997199564943597, 0.5000269303902773, -0.09990301618148731, 0.5000355797033876, -0.099794111454559, 0.5580704591046012, 0.09994460419614948, 0.500011265145089, -0.06952434001895211, 1.329997165077492, 0.0640036349943984, 0.5000016431953261, 11.87605960277668]]
GPR(X) =  (array([1.39114354]), array([0.94958038]))
```
### Versions

```shell
System:
    python: 3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]
executable: /home/karl/Documents/Gitlab/core/issues.testing/alex/venv_alex/bin/python3
   machine: Linux-5.15.0-60-generic-x86_64-with-glibc2.35

Python dependencies:
      sklearn: 1.2.1
          pip: 22.0.2
   setuptools: 59.6.0
        numpy: 1.24.2
        scipy: 1.10.0
       Cython: None
       pandas: 1.5.3
   matplotlib: 3.7.0
       joblib: 1.2.0
threadpoolctl: 3.1.0

Built with OpenMP: True

threadpoolctl info:
       user_api: openmp
   internal_api: openmp
         prefix: libgomp
       filepath: /home/karl/Documents/Gitlab/core/issues.testing/alex/venv_alex/lib/python3.10/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
        version: None
    num_threads: 8

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /home/karl/Documents/Gitlab/core/issues.testing/alex/venv_alex/lib/python3.10/site-packages/numpy.libs/libopenblas64_p-r0-15028c96.3.21.so
        version: 0.3.21
threading_layer: pthreads
   architecture: SkylakeX
    num_threads: 8

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /home/karl/Documents/Gitlab/core/issues.testing/alex/venv_alex/lib/python3.10/site-packages/scipy.libs/libopenblasp-r0-41284840.3.18.so
        version: 0.3.18
threading_layer: pthreads
   architecture: SkylakeX
    num_threads: 8
```

COMMENT:
Hi 👋 

could you extend the code snippet you provided with the steps needed to make it so that you can run it by copy and pasting the example into a fresh Python REPL? I think it is missing example data and the calls to use that data.

Labels: Bug, Needs Triage