title: Any difference between H2O and Scikit-Learn metrics scoring?
body: <p>I tried to use H2O to create some machine learning models for binary classification problem, and the test results are pretty good. But then I checked and found something weird. I tried to print the prediction of the model for the test set out of curiosity. And I found out that my model actually predicts 0 (negative) all the time, but the AUC is around 0.65, and precision is not 0.0. Then I tried to use Scikit-learn just to compare the metrics scores, and (as expected) they’re different. The Scikit learn yielded 0.0 precision and 0.5 AUC score, which I think is correct. Here's the code that I used:</p>
  
  <pre><code>model = h2o.load_model(model_path)
  predictions = model.predict(Test_data).as_data_frame()
  
  # H2O version to print the AUC score
  auc = model.model_performance(Test_data).auc()
  
  # Python version to print the AUC score
  auc_sklearn = sklearn.metrics.roc_auc_score(y_true, predictions['predict'].tolist())
  </code></pre>
  
  <p>Any thought? Thanks in advance!</p>