title: H2ONaiveBayesEstimator predicting unexpected result
body: <p>I have a data frame with four columns: 'a', 'b', 'c', 'target', where target is the sum of the first three values.
  I train H2ONaiveBayesEstimator with this data frame, and try to predict the result for a similar data frame. But the result is unexpected.</p>
  
  <p>I tried the same data frames with LinearRegression, and it gives the expected predictions.</p>
  
  <p>Why H2ONaiveBayesEstimator gives such a prediction result, value 27 for 20 rows?</p>
  
  <p>Code:</p>
  
  <pre class="lang-py prettyprint-override"><code>import pandas as pd
  import h2o
  from h2o.estimators import H2ONaiveBayesEstimator
  from sklearn.linear_model import LinearRegression
  
  train_df = pd.DataFrame(
      {
          "a": list(range(10)),
          "b": list(range(10)),
          "c": list(range(10)),
          "target": [i + i + i for i in list(range(10))]
      }
  )
  
  pred_df = pd.DataFrame(
      {
          "a": list(range(10, 30)),
          "b": list(range(10, 30)),
          "c": list(range(10, 30)),
      }
  )
  
  train_data = train_df.values
  features_data = train_df.drop('target', axis=1).values
  target_data = train_df['target'].values
  
  pred_data = pred_df.values
  
  lr = LinearRegression()
  lr.fit(features_data, target_data)
  predictions = lr.predict(pred_data)
  print(predictions)
  
  print('\n----\n')
  
  h2o.init()
  train_frame = h2o.H2OFrame(train_data, column_names=list(train_df.columns))
  nbm = H2ONaiveBayesEstimator()
  train_frame['target'] = train_frame['target'].asfactor()
  nbm.train(y='target', training_frame=train_frame)
  
  pred_frame = h2o.H2OFrame(pred_data, column_names=list(pred_df.columns))
  predictions = nbm.predict(pred_frame)
  print('\n----\n')
  print(predictions.as_data_frame())
  </code></pre>
  
  <p>train data frame:</p>
  
  <pre><code>   a  b  c  target
  0  0  0  0       0
  1  1  1  1       3
  2  2  2  2       6
  3  3  3  3       9
  4  4  4  4      12
  5  5  5  5      15
  6  6  6  6      18
  7  7  7  7      21
  8  8  8  8      24
  9  9  9  9      27
  </code></pre>
  
  <p>pred data frame:</p>
  
  <pre><code>0   10  10  10
  1   11  11  11
  2   12  12  12
  3   13  13  13
  4   14  14  14
  5   15  15  15
  6   16  16  16
  7   17  17  17
  8   18  18  18
  9   19  19  19
  10  20  20  20
  11  21  21  21
  12  22  22  22
  13  23  23  23
  14  24  24  24
  15  25  25  25
  16  26  26  26
  17  27  27  27
  18  28  28  28
  19  29  29  29
  </code></pre>
  
  <p>Result for H2ONaiveBayesEstimator:</p>
  
  <pre><code>    predict             p0             p3  ...           p21           p24       p27
  0        27   3.180305e-65   7.583358e-53  ...  6.076669e-06  1.098688e-02  0.989007
  1        27   6.040575e-77   2.893040e-63  ...  1.522156e-08  5.527786e-04  0.999447
  2        27   1.135940e-88   1.092736e-73  ...  3.775031e-11  2.753569e-05  0.999972
  3        27  2.135088e-100   4.125332e-84  ...  9.357610e-14  1.370957e-06  0.999999
  4        27  4.012965e-112   1.557370e-94  ...  2.319523e-16  6.825603e-08  1.000000
  5        27  7.542484e-124  5.879283e-105  ...  5.749522e-19  3.398268e-09  1.000000
  6        27  1.417632e-135  2.219508e-115  ...  1.425164e-21  1.691898e-10  1.000000
  7        27  2.664479e-147  8.378943e-126  ...  3.532629e-24  8.423464e-12  1.000000
  8        27  5.007966e-159  3.163164e-136  ...  8.756511e-27  4.193796e-13  1.000000
  9        27  9.412616e-171  1.194137e-146  ...  2.170522e-29  2.087968e-14  1.000000
  10       27  1.769128e-182  4.508027e-157  ...  5.380186e-32  1.039538e-15  1.000000
  11       27  3.325128e-194  1.701841e-167  ...  1.333615e-34  5.175555e-17  1.000000
  12       27  6.249673e-206  6.424678e-178  ...  3.305701e-37  2.576757e-18  1.000000
  13       27  1.174644e-217  2.425402e-188  ...  8.194013e-40  1.282892e-19  1.000000
  14       27  2.207777e-229  9.156221e-199  ...  2.031093e-42  6.387142e-21  1.000000
  15       27  4.149581e-241  3.456597e-209  ...  5.034575e-45  3.179971e-22  1.000000
  16       27  7.799257e-253  1.304912e-219  ...  1.247946e-47  1.583214e-23  1.000000
  17       27  1.465893e-264  4.926217e-230  ...  3.093350e-50  7.882360e-25  1.000000
  18       27  2.755188e-276  1.859713e-240  ...  7.667648e-53  3.924396e-26  1.000000
  19       27  5.178455e-288  7.020668e-251  ...  1.900620e-55  1.953842e-27  1.000000
  
  [20 rows x 11 columns]
  </code></pre>
  
  <p>Result for LinearRegression:</p>
  
  <pre><code>[30. 33. 36. 39. 42. 45. 48. 51. 54. 57. 60. 63. 66. 69. 72. 75. 78. 81.
   84. 87.]
  </code></pre>