[ENH] add CatBoostCvExperiment integration

[SimonBlanke]

The CatBoost estimator seems different enough from sklearn, that we should add a separate integration for it into Hyperactive. The CatBoostCvExperiment could look similar to this:

class CatBoostCvExperiment(BaseExperiment):
    _tags = {
        "python_dependencies": "catboost",
        "property:higher_or_lower_is_better": "lower",
    }
    def __init__(
        self,
        pool,
        iterations=100,
        fold_count=5,
        early_stopping_rounds=None,
        partition_random_seed=0,
        type="Classical",
    ):

[coder-jayp]

I'd like to work on this

My current plan:

• Add a CatBoostCvExperiment class inheriting from BaseExperiment
• Require a pool argument, along with the other CatBoost CV parameters mentioned above
• Use catboost.cv() internally to run the cross-validation instead of the sklearn-style .fit() flow

I’m aiming to keep the initial implementation minimal and aligned with the existing experiment interfaces, and then iterate based on feedback.

Please let me know if this approach looks reasonable, or if there’s an existing experiment class you’d recommend.