Move skl eval_metric and early_stopping rounds to model params. (#6751)

A new parameter `custom_metric` is added to `train` and `cv` to distinguish the behaviour from the old `feval`.  And `feval` is deprecated.  The new `custom_metric` receives transformed prediction when the built-in objective is used.  This enables XGBoost to use cost functions from other libraries like scikit-learn directly without going through the definition of the link function.

`eval_metric` and `early_stopping_rounds` in sklearn interface are moved from `fit` to `__init__` and is now saved as part of the scikit-learn model.  The old ones in `fit` function are now deprecated. The new `eval_metric` in `__init__` has the same new behaviour as `custom_metric`.

Added more detailed documents for the behaviour of custom objective and metric.

demo/guide-python/custom_rmsle.py

@@ -144,7 +144,7 @@ def py_rmsle(dtrain: xgb.DMatrix, dtest: xgb.DMatrix) -> Dict:
               dtrain=dtrain,
               num_boost_round=kBoostRound,
               obj=squared_log,
-              feval=rmsle,
+              custom_metric=rmsle,
               evals=[(dtrain, 'dtrain'), (dtest, 'dtest')],
               evals_result=results)