title: Why xgboost.cv and sklearn.cross_val_score give different results?
body: <p>I'm trying to make a classifier on a data set. I first used XGBoost:</p>
  
  <pre><code>import xgboost as xgb
  import pandas as pd
  import numpy as np
  
  train = pd.read_csv("train_users_processed_onehot.csv")
  labels = train["Buy"].map({"Y":1, "N":0})
  
  features = train.drop("Buy", axis=1)
  data_dmat = xgb.DMatrix(data=features, label=labels)
  
  params={"max_depth":5, "min_child_weight":2, "eta": 0.1, "subsamples":0.9, "colsample_bytree":0.8, "objective" : "binary:logistic", "eval_metric": "logloss"}
  rounds = 180
  
  result = xgb.cv(params=params, dtrain=data_dmat, num_boost_round=rounds, early_stopping_rounds=50, as_pandas=True, seed=23333)
  print result
  </code></pre>
  
  <p>And the result is:</p>
  
  <pre><code>        test-logloss-mean  test-logloss-std  train-logloss-mean  
  0             0.683539          0.000141            0.683407
  179           0.622302          0.001504            0.606452  
  </code></pre>
  
  <p>We can see it is around 0.622;</p>
  
  <p>But when I switch to <code>sklearn</code> using the exactly same parameters(I think), the result is quite different. Below is my code:</p>
  
  <pre><code>from sklearn.model_selection import cross_val_score
  from xgboost.sklearn import XGBClassifier
  import pandas as pd
  
  train_dataframe = pd.read_csv("train_users_processed_onehot.csv")
  train_labels = train_dataframe["Buy"].map({"Y":1, "N":0})
  train_features = train_dataframe.drop("Buy", axis=1)
  
  estimator = XGBClassifier(learning_rate=0.1, n_estimators=190, max_depth=5, min_child_weight=2, objective="binary:logistic", subsample=0.9, colsample_bytree=0.8, seed=23333)
  print cross_val_score(estimator, X=train_features, y=train_labels, scoring="neg_log_loss")
  </code></pre>
  
  <p>and the result is:<code>[-4.11429976 -2.08675843 -3.27346662]</code>, after reversing it is still far from 0.622.</p>
  
  <p>I tossed a break point into <code>cross_val_score</code>, and saw that the classifier is making crazy predictions by trying to predict every tuple in the test set to be negative with about 0.99 probability. </p>
  
  <p>I'm wondering where have I gone wrong. Could someone help me?</p>