title: Error in running h2o.ensemble
body: <p>I am getting error while running h2o.ensemble in R. This is the error output </p>
  
  <pre><code>[1] "Cross-validating and training base learner 1: h2o.glm.wrapper"
    |======================================================================| 100%
  [1] "Cross-validating and training base learner 2: h2o.randomForest.1"
    |==============                                                        |  19%
  
  Got exception 'class java.lang.AssertionError', with msg 'null'
  java.lang.AssertionError
      at hex.tree.DHistogram.scoreMSE(DHistogram.java:323)
      at hex.tree.DTree$DecidedNode$FindSplits.compute2(DTree.java:441)
      at hex.tree.DTree$DecidedNode.bestCol(DTree.java:421)
      at hex.tree.DTree$DecidedNode.&lt;init&gt;(DTree.java:449)
      at hex.tree.SharedTree.makeDecided(SharedTree.java:489)
      at hex.tree.SharedTree$ScoreBuildOneTree.onCompletion(SharedTree.java:436)
      at jsr166y.CountedCompleter.__tryComplete(CountedCompleter.java:425)
      at jsr166y.CountedCompleter.tryComplete(CountedCompleter.java:383)
      at water.MRTask.compute2(MRTask.java:683)
      at water.H2O$H2OCountedCompleter.compute(H2O.java:1069)
      at jsr166y.CountedCompleter.exec(CountedCompleter.java:468)
      at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263)
      at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974)
      at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477)
      at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104)
  
  
  Error: 'null'
  </code></pre>
  
  <p>This is my code that i am using. I am using this script for regression problem. "sales" column is for output prediction. Rest of the columns are for training. </p>
  
  <pre><code>response &lt;- "Sales"
  predictors &lt;- setdiff(names(train), response)
  
  h2o.glm.1 &lt;- function(..., alpha = 0.0) h2o.glm.wrapper(..., alpha = alpha)
  h2o.glm.2 &lt;- function(..., alpha = 0.5) h2o.glm.wrapper(..., alpha = alpha)
  h2o.glm.3 &lt;- function(..., alpha = 1.0) h2o.glm.wrapper(..., alpha = alpha)
  h2o.randomForest.1 &lt;- function(..., ntrees = 200, nbins = 50, seed = 1) h2o.randomForest.wrapper(..., ntrees = ntrees, nbins = nbins, seed = seed)
  h2o.randomForest.2 &lt;- function(..., ntrees = 200, sample_rate = 0.75, seed = 1) h2o.randomForest.wrapper(..., ntrees = ntrees, sample_rate = sample_rate, seed = seed)
  h2o.gbm.1 &lt;- function(..., ntrees = 100, seed = 1) h2o.gbm.wrapper(..., ntrees = ntrees, seed = seed)
  h2o.gbm.6 &lt;- function(..., ntrees = 100, col_sample_rate = 0.6, seed = 1) h2o.gbm.wrapper(..., ntrees = ntrees, col_sample_rate = col_sample_rate, seed = seed)
  h2o.gbm.8 &lt;- function(..., ntrees = 100, max_depth = 3, seed = 1) h2o.gbm.wrapper(..., ntrees = ntrees, max_depth = max_depth, seed = seed)
  h2o.deeplearning.1 &lt;- function(..., hidden = c(500,500), activation = "Rectifier", epochs = 50, seed = 1)  h2o.deeplearning.wrapper(..., hidden = hidden, activation = activation, seed = seed)
  h2o.deeplearning.6 &lt;- function(..., hidden = c(50,50), activation = "Rectifier", epochs = 50, seed = 1)  h2o.deeplearning.wrapper(..., hidden = hidden, activation = activation, seed = seed)
  h2o.deeplearning.7 &lt;- function(..., hidden = c(100,100), activation = "Rectifier", epochs = 50, seed = 1)  h2o.deeplearning.wrapper(..., hidden = hidden, activation = activation, seed = seed)
  
  print("learning starts ")
  #### Customized base learner library
  learner &lt;- c("h2o.glm.wrapper",
               "h2o.randomForest.1", "h2o.randomForest.2",
               "h2o.gbm.1", "h2o.gbm.6", "h2o.gbm.8",
               "h2o.deeplearning.1", "h2o.deeplearning.6", "h2o.deeplearning.7")
  
  metalearner &lt;- "h2o.glm.wrapper"
  #
  #Train with new library:
  fit &lt;- h2o.ensemble(
    x =  predictors, 
    y= response,
    training_frame=train,
    family = "gaussian", 
    learner = learner, 
    metalearner = metalearner,
    cvControl = list(V = 5))
  </code></pre>
  
  <p>All columns of train data are numeral. I am using R version 3.2.2. </p>