diff --git a/wrapper/xgboost.py b/wrapper/xgboost.py
index 5c8199c4d..7919686f6 100644
--- a/wrapper/xgboost.py
+++ b/wrapper/xgboost.py
@@ -764,20 +764,27 @@ class XGBModel(BaseEstimator):
 
     nthread : int
         Number of parallel threads used to run xgboost.
+    gamma : float
+        Minimum loss reduction required to make a further partition on a leaf node of the tree.
     min_child_weight : int
-        minimum sum of instance weight(hessian) needed in a child.        
+        Minimum sum of instance weight(hessian) needed in a child.        
+    max_delta_step : int
+        Maximum delta step we allow each tree's weight estimation to be.
     subsample : float
         Subsample ratio of the training instance.
     colsample_bytree : float
         Subsample ratio of columns when constructing each tree.
+
+    base_score:
+        The initial prediction score of all instances, global bias.
     eval_metric : string
         Evaluation metrics for validation data.
     seed : int
         Random number seed.
     """
     def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="reg:linear", 
-                 nthread=-1, min_child_weight=1, subsample=1, colsample_bytree=1, 
-                 eval_metric='error', seed=0):
+                 nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, 
+                 base_score=0.5, eval_metric='error', seed=0):
         if not SKLEARN_INSTALLED:
             raise Exception('sklearn needs to be installed in order to use this module')
         self.max_depth = max_depth
@@ -787,9 +794,13 @@ class XGBModel(BaseEstimator):
         self.objective = objective
         
         self.nthread = nthread
+        self.gamma = gamma
         self.min_child_weight = min_child_weight
+        self.max_delta_step = max_delta_step
         self.subsample = subsample
         self.colsample_bytree = colsample_bytree
+
+        self.base_score = base_score
         self.eval_metric = eval_metric
         self.seed = seed
         
@@ -816,11 +827,11 @@ class XGBModel(BaseEstimator):
 
 class XGBClassifier(XGBModel, ClassifierMixin):
     def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="binary:logistic", 
-                 nthread=-1, min_child_weight=1, subsample=1, colsample_bytree=1,
-                 eval_metric='error', seed=0):
+                 nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, 
+                 base_score=0.5, eval_metric='error', seed=0):
         super(XGBClassifier, self).__init__(max_depth, learning_rate, n_estimators, silent, objective, 
-                                            nthread, min_child_weight, subsample, colsample_bytree,
-                                            eval_metric, seed)
+                                            nthread, gamma, min_child_weight, max_delta_step, subsample, colsample_bytree,
+                                            base_score, eval_metric, seed)
 
     def fit(self, X, y, sample_weight=None):
         y_values = list(np.unique(y))