TST sklearn/linear_model/tests/test_quantile.py::test_sparse_input fails on local windows

The test ``test_sparse_input`` from ``sklearn/linear_model/tests/test_quantile.py`` fails when I run the test suite locally (Windows).

It fails for the ``fit_intercept=True`` parametrization but passes for ``fit_intercept=False``. It fails for all solvers. Here's the error message
```py
>           assert 0.45 <= np.mean(y < quant_sparse.predict(X_sparse)) <= 0.55
E           AssertionError: assert 0.56 <= 0.55
```

Maybe 0.55 was too tight ?
Ping @lorentzenchr who reviewed the PR introducing this test https://github.com/scikit-learn/scikit-learn/pull/21086

```
System:
    python: 3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:50:36) [MSC v.1929 64 bit (AMD64)]
executable: C:\Users\J\miniconda3\envs\dev\python.exe
   machine: Windows-10-10.0.19045-SP0

Python dependencies:
      sklearn: 1.3.dev0
          pip: 22.2.2
   setuptools: 65.3.0
        numpy: 1.23.3
        scipy: 1.9.2
       Cython: 0.29.33
       pandas: 1.4.4
   matplotlib: 3.5.3
       joblib: 1.2.0
threadpoolctl: 3.1.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: mkl
         prefix: libblas
       filepath: C:\Users\J\miniconda3\envs\dev\Library\bin\libblas.dll
        version: 2022.1-Product
threading_layer: intel
    num_threads: 8

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: C:\Users\J\miniconda3\envs\dev\Lib\site-packages\scipy.libs\libopenblas-57db09cfe174768fb409a6bb5a530d4c.dll
        version: 0.3.18
threading_layer: pthreads
   architecture: Zen
    num_threads: 16

       user_api: openmp
   internal_api: openmp
         prefix: vcomp
       filepath: C:\Users\J\miniconda3\envs\dev\vcomp140.dll
        version: None
    num_threads: 16
```

COMMENT:
Hey, I tried reproducing the error on my Windows laptop. I made a slight change in `test_sparse_input`. For me it fails when `fit_intercept=False`:

```
def test_sparse_input(sparse_format, solver, fit_intercept, default_solver):
    """Test that sparse and dense X give same results."""
    X, y = make_regression(n_samples=100, n_features=20, random_state=1, noise=1.0)
    X_sparse = sparse_format(X)
    alpha = 1e-4
    quant_dense = QuantileRegressor(
        alpha=alpha, fit_intercept=False, solver=default_solver
    ).fit(X, y)
    quant_sparse = QuantileRegressor(
        alpha=alpha, fit_intercept=False, solver=solver
    ).fit(X_sparse, y)
    assert_allclose(quant_sparse.coef_, quant_dense.coef_, rtol=1e-2)
    if fit_intercept:
        assert quant_sparse.intercept_ == approx(quant_dense.intercept_)
        # check that we still predict fraction
        assert 0.45 <= np.mean(y < quant_sparse.predict(X_sparse)) <= 0.55
```

The test fails as follows:
```
sklearn\linear_model\tests\test_quantile.py FFFFFFFFF.........                                                   [100%]

>           assert 0.45 <= np.mean(y < quant_sparse.predict(X_sparse)) <= 0.55
E           AssertionError: assert 0.57 <= 0.55
E            +  where 0.57 = <function mean at 0x00000130D6E8D310>(array([-3.01531123e+02,  7.51750268e+01, -1.32031414e+02, -7.65705861e-01,\n       -1.43455883e+02, -1.60277844e+02, -7...0011e+01,  2.64334409e+01, -8.86220005e+01,\n        2.20989098e+02, -1.21922232e+02,  1.97801750e+02,  1.17310988e+01]) < array([-3.02973610e+02,  7.61644952e+01, -1.32031414e+02, -7.77706114e-01,\n       -1.43455883e+02, -1.61976509e+02, -7...7304e+01,  2.47161363e+01, -8.82817578e+01,\n        2.20945479e+02, -1.20169706e+02,  1.97825826e+02,  9.93946011e+00]))
E            +    where <function mean at 0x00000130D6E8D310> = np.mean
E            +    and   array([-3.02973610e+02,  7.61644952e+01, -1.32031414e+02, -7.77706114e-01,\n       -1.43455883e+02, -1.61976509e+02, -7...7304e+01,  2.47161363e+01, -8.82817578e+01,\n        2.20945479e+02, -1.20169706e+02,  1.97825826e+02,  9.93946011e+00]) = <bound method LinearModel.predict of QuantileRegressor(alpha=0.0001, fit_intercept=False, solver='highs-ipm')>(<100x20 sparse matrix of type '<class 'numpy.float64'>'\n   with 2000 stored elements in COOrdinate format>)
E            +      where <bound method LinearModel.predict of QuantileRegressor(alpha=0.0001, fit_intercept=False, solver='highs-ipm')> = QuantileRegressor(alpha=0.0001, fit_intercept=False, solver='highs-ipm').predict

sklearn\linear_model\tests\test_quantile.py:279: AssertionError
============================================= 9 failed, 9 passed in 1.08s =============================================
```

System:

```
System:
    python: 3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:51:29) [MSC v.1929 64 bit (AMD64)]
executable: C:\Users\rahil\anaconda3\envs\sklearn-env\python.exe
   machine: Windows-10-10.0.22621-SP0

Python dependencies:
      sklearn: 1.3.dev0
          pip: 22.1.2
   setuptools: 62.3.2
        numpy: 1.22.4
        scipy: 1.8.1
       Cython: 0.29.33
       pandas: 2.0.0
   matplotlib: 3.7.1
       joblib: 1.2.0
threadpoolctl: 3.1.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: mkl
         prefix: libblas
       filepath: C:\Users\rahil\anaconda3\envs\sklearn-env\Library\bin\libblas.dll
        version: 2022.0-Product
threading_layer: intel
    num_threads: 4

       user_api: openmp
   internal_api: openmp
         prefix: vcomp
       filepath: C:\Users\rahil\anaconda3\envs\sklearn-env\vcomp140.dll
        version: None
    num_threads: 8
```

Changing the upper bound from 0.55 to 0.57 allows the tests to execute successfully on my laptop. If it is possible to change the limits, can I make a PR for this?

Labels: module:test-suite