Add classification decision tree with support for entropy and gini index

[anikchand461]

Feature description

The existing decision tree implementation supports only regression using mean squared error. This PR introduces a classification decision tree that supports multiple splitting criteria, including entropy (information gain) and gini index. This enhances flexibility and aligns with standard decision tree implementations. The code will include type hints, doctests, and proper validation.