From c23783a0d1db3d795befe8e4588f211465b82a3f Mon Sep 17 00:00:00 2001
From: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
Date: Sun, 7 Oct 2018 14:09:09 -0700
Subject: [PATCH] Add notes to doc (#3765)

---
 python-package/xgboost/core.py    | 20 ++++++++++++++++++++
 python-package/xgboost/sklearn.py |  6 ++++++
 2 files changed, 26 insertions(+)

diff --git a/python-package/xgboost/core.py b/python-package/xgboost/core.py
index 062627365..30ab6125d 100644
--- a/python-package/xgboost/core.py
+++ b/python-package/xgboost/core.py
@@ -875,6 +875,10 @@ class Booster(object):
                                          ctypes.byref(self.handle)))
         self.set_param({'seed': 0})
         self.set_param(params or {})
+        if (params is not None) and ('booster' in params):
+            self.booster = params['booster']
+        else:
+            self.booster = 'gbtree'
         if model_file is not None:
             self.load_model(model_file)
 
@@ -1379,6 +1383,12 @@ class Booster(object):
     def get_fscore(self, fmap=''):
         """Get feature importance of each feature.
 
+        .. note:: Feature importance is defined only for tree boosters
+
+            Feature importance is only defined when the decision tree model is chosen as base
+            learner (`booster=gbtree`). It is not defined for other base learner types, such
+            as linear learners (`booster=gblinear`).
+
         Parameters
         ----------
         fmap: str (optional)
@@ -1397,6 +1407,12 @@ class Booster(object):
         * 'total_gain': the total gain across all splits the feature is used in.
         * 'total_cover': the total coverage across all splits the feature is used in.
 
+        .. note:: Feature importance is defined only for tree boosters
+
+            Feature importance is only defined when the decision tree model is chosen as base
+            learner (`booster=gbtree`). It is not defined for other base learner types, such
+            as linear learners (`booster=gblinear`).
+
         Parameters
         ----------
         fmap: str (optional)
@@ -1405,6 +1421,10 @@ class Booster(object):
             One of the importance types defined above.
         """
 
+        if self.booster != 'gbtree':
+            raise ValueError('Feature importance is not defined for Booster type {}'
+                             .format(self.booster))
+
         allowed_importance_types = ['weight', 'gain', 'cover', 'total_gain', 'total_cover']
         if importance_type not in allowed_importance_types:
             msg = ("importance_type mismatch, got '{}', expected one of " +
diff --git a/python-package/xgboost/sklearn.py b/python-package/xgboost/sklearn.py
index 0b57cbd30..047aa15f2 100644
--- a/python-package/xgboost/sklearn.py
+++ b/python-package/xgboost/sklearn.py
@@ -466,6 +466,12 @@ class XGBModel(XGBModelBase):
         """
         Feature importances property
 
+        .. note:: Feature importance is defined only for tree boosters
+
+            Feature importance is only defined when the decision tree model is chosen as base
+            learner (`booster=gbtree`). It is not defined for other base learner types, such
+            as linear learners (`booster=gblinear`).
+
         Returns
         -------
         feature_importances_ : array of shape ``[n_features]``