title: Titanic Dataset overfitting: can it be that much?
body: <p>I am a bit confused as I am training a model that yields circa 88% CV score on the train data while the same model performs poorly on the test data after I submit it ( score of 0.75).
  This drop of 12 points in accuracy can't be all due to overfitting, no?
  Any ideas? Have you experienced such a gap in your models/submissions?</p>
  <p>See the enclosed image for the model and results.</p>
  <p>##########################################################</p>
  <pre><code>xgb_clf = XGBClassifier(n_estimators=87, learning_rate=0.05,max_depth = 10,
  colsample_bytree =0.8 , n_jobs=-1 , random_state = 7,
  scale_pos_weight = 0.6, min_child_weight = 0.9, gamma = 2.1)
  skf = RepeatedStratifiedKFold(n_splits = 4)
  
  results= cross_val_score(xgb_clf , X_train , y , cv =skf, scoring='roc_auc')
  print(&quot;Accuracy: %.2f%% (%.2f%%)&quot; % (results.mean()100, results.std()100))
  </code></pre>
  <h1></h1>
  <p>Accuracy: 88.13% (2.47%)</p>