[breaking][skl] Remove parameter serialization. (#8963)

- Remove parameter serialization in the scikit-learn interface.

The scikit-lear interface `save_model` will save only the model and discard all
hyper-parameters. This is to align with the native XGBoost interface, which distinguishes
the hyper-parameter and model parameters.

With the scikit-learn interface, model parameters are attributes of the estimator. For
instance, `n_features_in_`, `n_classes_` are always accessible with
`estimator.n_features_in_` and `estimator.n_classes_`, but not with the
`estimator.get_params`.

- Define a `load_model` method for classifier to load its own attributes.

- Set n_estimators to None by default.

tests/python/test_model_compatibility.py

@@ -66,7 +66,6 @@ def run_scikit_model_check(name, path):
         cls.load_model(path)
         if name.find('0.90') == -1:
             assert len(cls.classes_) == gm.kClasses
-            assert len(cls._le.classes_) == gm.kClasses
             assert cls.n_classes_ == gm.kClasses
         assert (len(cls.get_booster().get_dump()) ==
                 gm.kRounds * gm.kForests * gm.kClasses), path