[python][docs] add info on adaptive learning rate in the sklearn API (#1354)

* add info on adaptive learning rate in the sklearn API

* adjust learning rate documentation following the PR discussion

* fix early stopping documentation

* improve wording

* fixing trailing spaces

docs/Python-Intro.rst

@@ -196,8 +196,7 @@ The model will train until the validation score stops improving.
 Validation error needs to improve at least every ``early_stopping_rounds`` to continue training.
 
 If early stopping occurs, the model will have an additional field: ``bst.best_iteration``.
-Note that ``train()`` will return a model from the last iteration, not the best one.
-And you can set ``num_iteration=bst.best_iteration`` when saving model.
+Note that ``train()`` will return a model from the best iteration.
 
 This works with both metrics to minimize (L2, log loss, etc.) and to maximize (NDCG, AUC).
 Note that if you specify more than one evaluation metric, all of them will be used for early stopping.

python-package/lightgbm/sklearn.py

@@ -149,6 +149,9 @@ class LGBMModel(_LGBMModelBase):
             Maximum tree depth for base learners, -1 means no limit.
         learning_rate : float, optional (default=0.1)
             Boosting learning rate.
+            You can use ``callbacks`` parameter of ``fit`` method to shrink/adapt learning rate
+            in training using ``reset_parameter`` callback.
+            Note, that this will ignore the ``learning_rate`` argument in training.
         n_estimators : int, optional (default=100)
             Number of boosted trees to fit.
         subsample_for_bin : int, optional (default=50000)