diff --git a/wrapper/xgboost.py b/wrapper/xgboost.py
index 25d2827db..33ee9565a 100644
--- a/wrapper/xgboost.py
+++ b/wrapper/xgboost.py
@@ -26,7 +26,6 @@ except ImportError:
     SKLEARN_INSTALLED = False
 
 
-
 __all__ = ['DMatrix', 'CVPack', 'Booster', 'aggcv', 'cv', 'mknfold', 'train']
 
 if sys.version_info[0] == 3:
@@ -619,7 +618,7 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, ea
 
             score = float(msg.rsplit(':', 1)[1])
             if (maximize_score and score > best_score) or \
-                (not maximize_score and score < best_score):
+                    (not maximize_score and score < best_score):
                 best_score = score
                 best_score_i = i
                 best_msg = msg
@@ -632,7 +631,6 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, ea
         return bst
 
 
-
 class CVPack(object):
     def __init__(self, dtrain, dtest, param):
         self.dtrain = dtrain
@@ -778,6 +776,7 @@ class XGBModel(BaseEstimator):
                 'silent': True if self.silent == 1 else False,
                 'objective': self.objective
                 }
+
     def get_xgb_params(self):
         return {'eta': self.eta, 'max_depth': self.max_depth, 'silent': self.silent, 'objective': self.objective}
 
@@ -790,6 +789,7 @@ class XGBModel(BaseEstimator):
         testDmatrix = DMatrix(X)
         return self._Booster.predict(testDmatrix)
 
+
 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")
@@ -834,9 +834,8 @@ class XGBClassifier(XGBModel, ClassifierMixin):
         else:
             classone_probs = class_probs
             classzero_probs = 1.0 - classone_probs
-            return np.vstack((classzero_probs,classone_probs)).transpose()
+            return np.vstack((classzero_probs, classone_probs)).transpose()
+
 
 class XGBRegressor(XGBModel, RegressorMixin):
     pass
-
-