Support CatBoost feature importances in Explainer

[aman-coder03]

Proposed Changes

this pr adds support for using CatBoostRegressor as the model_tau estimator in the Explainer class when method="auto"

what changed

• added a small private helper to retrieve feature importances from different tree-based models
• the helper supports,
  • feature_importances_ for scikit-learn, lightgbm, and xgboost
  • get_feature_importance() for catboost
• updated check_conditions() to use this helper after fitting the dummy model
• updated default_importance() to use the helper instead of directly accessing feature_importances_
• updated the model_tau docstring to mention catboost support
• added a test using CatBoostRegressor (guarded with pytest.importorskip("catboost")) to verify that Explainer(method="auto") works correctly and returns feature importances with the expected shape

why

currently Explainer assumes that every supported model exposes feature importances through the feature_importances_ attribute. however, catboost provides feature importances through get_feature_importance() instead. because of this, catboost models fail the validation in check_conditions() and cannot be used with method="auto"

closes #826

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