address review comments

python-package/lightgbm/dask.py

@@ -387,7 +387,7 @@ def _predict(model, data, raw_score=False, pred_proba=False, pred_leaf=False, pr
 class _LGBMModel:
     def __init__(self):
         if not all((DASK_INSTALLED, PANDAS_INSTALLED, SKLEARN_INSTALLED)):
-            raise LightGBMError('Dask, Pandas and Scikit-learn are required for this module')
+            raise LightGBMError('dask, pandas and scikit-learn are required for lightgbm.dask')
 
     def _fit(self, model_factory, X, y=None, sample_weight=None, group=None, client=None, **kwargs):
         """Docstring is inherited from the LGBMModel."""

python-package/lightgbm/engine.py

@@ -335,15 +335,15 @@ def _make_n_folds(full_data, folds, nfold, params, seed, fpreproc=None, stratifi
                                              "xe_ndcg", "xe_ndcg_mart", "xendcg_mart"}
                for obj_alias in _ConfigAliases.get("objective")):
             if not SKLEARN_INSTALLED:
-                raise LightGBMError('Scikit-learn is required for ranking cv.')
+                raise LightGBMError('scikit-learn is required for ranking cv')
             # ranking task, split according to groups
             group_info = np.array(full_data.get_group(), dtype=np.int32, copy=False)
             flatted_group = np.repeat(range(len(group_info)), repeats=group_info)
             group_kfold = _LGBMGroupKFold(n_splits=nfold)
             folds = group_kfold.split(X=np.zeros(num_data), groups=flatted_group)
         elif stratified:
             if not SKLEARN_INSTALLED:
-                raise LightGBMError('Scikit-learn is required for stratified cv.')
+                raise LightGBMError('scikit-learn is required for stratified cv')
             skf = _LGBMStratifiedKFold(n_splits=nfold, shuffle=shuffle, random_state=seed)
             folds = skf.split(X=np.zeros(num_data), y=full_data.get_label())
         else:

python-package/lightgbm/sklearn.py

@@ -290,7 +290,7 @@ def __init__(self, boosting_type='gbdt', num_leaves=31, max_depth=-1,
         and you should group grad and hess in this way as well.
         """
         if not SKLEARN_INSTALLED:
-            raise LightGBMError('Scikit-learn is required for this module')
+            raise LightGBMError('scikit-learn is required for lightgbm.sklearn')
 
         self.boosting_type = boosting_type
         self.objective = objective