title: Postgres-MADlib predictions is taking longer than training
body: <p>I am training my data using following code:</p>
  
  <pre><code>start_time := clock_timestamp();
    PERFORM madlib.create_nb_prepared_data_tables( 'nb_training',
                                                   'class', 
                                                   'attributes', 
                                                   'ARRAY[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57]', 
                                                   57, 
                                                   'categ_feature_probs', 
                                                   'numeric_attr_params', 
                                                   'class_priors' 
                                                 );
    training_time := 1000* (extract(epoch FROM clock_timestamp()) - extract(epoch FROM start_time));
  </code></pre>
  
  <p>And my prediction code goes as follows: </p>
  
  <pre><code>start_time := clock_timestamp();
    PERFORM madlib.create_nb_probs_view( 'categ_feature_probs', 
                                         'class_priors', 
                                         'nb_testing', 
                                         'id', 
                                         'attributes', 
                                         57, 
                                         'numeric_attr_params', 
                                         'probs_view' );
  
  select * from probs_view
  prediction_time := 1000 * (extract(epoch FROM clock_timestamp()) - extract(epoch FROM start_time));
  </code></pre>
  
  <p>The training data is containing 450000 records were as testing dataset contains 50000 records. </p>
  
  <p>Still, my average training_time is around 17173 ms where as prediction_time is 26481 ms. As per my understanding of naive bayes, the prediction_time should be less than training_time. What am I doing wrong here?</p>