Describe the bug

In parameter validation there are many places where we use ["array-like", "sparse matrix"] so I think at least the former should not be a superset of the latter, but it is the case now. Looking at the class _ArrayLikes, it treats the input as valid as long as the input has __len__, shape, or __array__ and is not a scaler. Clearly both sparse matrices and sparse arrays satisfy this condition, though I think they should be excluded. I propose adding the constraint not sp.issparse(array) to "array-like".

For more context please see #27950 which tries to extend parameter validation to the new sparse arrays.

Also quoting the glossary page

Steps/Code to Reproduce

>>> from sklearn.utils._param_validation import validate_params
>>> @validate_params({"X": ["array-like"]}, prefer_skip_nested_validation=False)
... def func(X):
...     return X
...
>>> import scipy.sparse as sp
>>> func(sp.csr_array((3, 4)))
<3x4 sparse array of type '<class 'numpy.float64'>'
        with 0 stored elements in Compressed Sparse Row format>
>>> func(sp.csr_matrix((3, 4)))
<3x4 sparse matrix of type '<class 'numpy.float64'>'
        with 0 stored elements in Compressed Sparse Row format>

Another example can be AgglomerativeClustering, where the validation for connectivity does not include "sparse matrix" but tests such as sklearn/cluster/tests/test_hierarchical.py::test_agglomerative_clustering are passing even though connectivity is sparse.

Expected Results

Both should raise sklearn.utils._param_validation.InvalidParameterError: The 'X' parameter of func must be an array-like.

Actual Results

No error.

Versions

System:
    python: 3.9.18 | packaged by conda-forge | (main, Aug 30 2023, 03:40:31) [MSC v.1929 64 bit (AMD64)]
executable: D:\Downloads\mambaforge\envs\sklearn-env\python.exe
   machine: Windows-10-10.0.19045-SP0

Python dependencies:
      sklearn: 1.5.dev0
          pip: 23.2.1
   setuptools: 68.2.2
        numpy: 1.26.0
        scipy: 1.11.2
       Cython: 3.0.2
       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: mkl
    num_threads: 6
         prefix: libblas
       filepath: D:\Downloads\mambaforge\envs\sklearn-env\Library\bin\libblas.dll
        version: 2022.1-Product
threading_layer: intel

       user_api: openmp
   internal_api: openmp
    num_threads: 12
         prefix: vcomp
       filepath: D:\Downloads\mambaforge\envs\sklearn-env\vcomp140.dll
        version: None