diff --git a/wrapper/xgboost.py b/wrapper/xgboost.py
index 33ee9565a..1906ce09e 100644
--- a/wrapper/xgboost.py
+++ b/wrapper/xgboost.py
@@ -763,26 +763,30 @@ class XGBModel(BaseEstimator):
         if not SKLEARN_INSTALLED:
             raise Exception('sklearn needs to be installed in order to use this module')
         self.max_depth = max_depth
-        self.eta = learning_rate
-        self.silent = 1 if silent else 0
-        self.n_rounds = n_estimators
+        self.learning_rate = learning_rate
+        self.silent = silent
+        self.n_estimators = n_estimators
         self.objective = objective
         self._Booster = Booster()
 
     def get_params(self, deep=True):
         return {'max_depth': self.max_depth,
-                'learning_rate': self.eta,
-                'n_estimators': self.n_rounds,
-                'silent': True if self.silent == 1 else False,
+                'learning_rate': self.learning_rate,
+                'n_estimators': self.n_estimators,
+                'silent': self.silent,
                 'objective': self.objective
                 }
 
     def get_xgb_params(self):
-        return {'eta': self.eta, 'max_depth': self.max_depth, 'silent': self.silent, 'objective': self.objective}
+        return {'eta': self.learning_rate,
+                'max_depth': self.max_depth,
+                'silent': 1 if self.silent else 0,
+                'objective': self.objective
+                }
 
     def fit(self, X, y):
         trainDmatrix = DMatrix(X, label=y)
-        self._Booster = train(self.get_xgb_params(), trainDmatrix, self.n_rounds)
+        self._Booster = train(self.get_xgb_params(), trainDmatrix, self.n_estimators)
         return self
 
     def predict(self, X):
@@ -791,8 +795,8 @@ class XGBModel(BaseEstimator):
 
 
 class XGBClassifier(XGBModel, ClassifierMixin):
-    def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True):
-        super(XGBClassifier, self).__init__(max_depth, learning_rate, n_estimators, silent, objective="binary:logistic")
+    def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="binary:logistic"):
+        super(XGBClassifier, self).__init__(max_depth, learning_rate, n_estimators, silent, objective)
 
     def fit(self, X, y, sample_weight=None):
         y_values = list(np.unique(y))
@@ -812,7 +816,7 @@ class XGBClassifier(XGBModel, ClassifierMixin):
         else:
             trainDmatrix = DMatrix(X, label=training_labels)
 
-        self._Booster = train(xgb_options, trainDmatrix, self.n_rounds)
+        self._Booster = train(xgb_options, trainDmatrix, self.n_estimators)
 
         return self