title: Interpretation of AUC NaN values in h2o cross-validation predictions summary
body: <p>I have noticed that for some runs of:</p>
  
  <pre><code>train=as.h2o(u)
  mod = h2o.glm(family= "binomial", x= c(1:15), y="dc",  
         training_frame=train, missing_values_handling = "Skip",
         lambda = 0, compute_p_values = TRUE, nfolds = 10,
         keep_cross_validation_predictions= TRUE)
  </code></pre>
  
  <p>there are NaNs in cross-validation metrics summary of AUC for some cv iterations of the model.</p>
  
  <p>For example:</p>
  
  <pre><code>print(mod@model$cross_validation_metrics_summary["auc",])
  </code></pre>
  
  <p>Cross-Validation Metrics Summary: </p>
  
  <pre><code>          mean         sd cv_1_valid cv_2_valid cv_3_valid cv_4_valid cv_5_valid cv_6_valid cv_7_valid cv_8_valid cv_9_valid cv_10_valid
  
  auc 0.63244045 0.24962118       0.25  0.6666667  0.8095238        1.0  0.6666667 0.46666667        NaN        NaN        1.0         0.2
  </code></pre>
  
  <p>NaN in CV seems to appear less frequently when I set smaller nfolds=7.</p>
  
  <p>How these NaN values should be interpreted and when h2o cross-validation outputs them?</p>
  
  <p>I suppose it happens when AUC can't be assessed correctly in an iteration. My training set has 70 complete rows.</p>
  
  <p>Can such AUC cross-validation results (containing NaNs) be considered as reliable?</p>