ok

python/example/demo.py

@@ -76,8 +76,15 @@ def logregobj(preds, dtrain):
     return grad, hess
 
 # user defined evaluation function, return a pair metric_name, result
+# NOTE: when you do customized loss function, the default prediction value is margin
+# this may make buildin evalution metric not function properly
+# for example, we are doing logistic loss, the prediction is score before logistic transformation
+# the buildin evaluation error assumes input is after logistic transformation
+# Take this in mind when you use the customization, and maybe you need write customized evaluation function
 def evalerror(preds, dtrain):
     labels = dtrain.get_label()
+    # return a pair metric_name, result
+    # since preds are margin(before logistic transformation, cutoff at 0)
     return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
 
 # training with customized objective, we can also do step by step training

python/xgboost.py

@@ -226,9 +226,6 @@ def train(params, dtrain, num_boost_round = 10, evals = [], obj=None, feval=None
             if len(evals) != 0:
                 sys.stderr.write(evaluate(bst, evals, i, feval)+'\n')
     else:
-        if len(evals) != 0 and feval == None:
-            print 'you need to provide your own evaluation function'
-
         # try customized objective function
         for i in range(num_boost_round):
             pred = bst.predict( dtrain )