Feature solver

[rbaral]

Context:
This PR provides the solution to the issue Linear Selection Method fails with Python 3.12 (#24). The issue is about the Linear selection failing because the scikit-learn does not support multi_class attribute for LogisticRegression

Traceback (most recent call last):
  File "/home/rbaral/Documents/projects/selective/sample.py", line 13, in <module>
    selector = Selective(SelectionMethod.Linear(num_features=3, regularization="none"))
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/rbaral/Documents/projects/selective/feature/selector.py", line 532, in __init__
    self._imp = _Linear(self.seed, self.selection_method.num_features,
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/rbaral/Documents/projects/selective/feature/linear.py", line 32, in __init__
    "classification_none": LogisticRegression(random_state=self.seed,
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: LogisticRegression.__init__() got an unexpected keyword argument 'multi_class'

See more context and replication steps at #24

Fix:

1. In feature/linear.py, inside __init__() method, use the OneVsRestClassifier to wrap the LogisticRegression and remove the multi_class attribute.
2. In text_based.py, update the way the Label matrix is defined inside process_category_data() method

@takojunior @kuppulur @skadio @wddcheng Can you please help with the PR review?

[wddcheng]

LGTM!

[skadio]

Checking if this approval works/possible.

[skadio]

(let me know if you need help in updating pypi)