title: Predict classes or class probabilities?
body: <p>I am currently using H2O for a classification problem dataset. I am testing it out with <code>H2ORandomForestEstimator</code> in a python 3.6 environment. I noticed the results of the predict method was giving values between 0 to 1(I am assuming this is the probability). </p>
  
  <p>In my data set, the target attribute is numeric i.e. <code>True</code> values are 1 and <code>False</code> values are 0. I made sure I converted the type to category for the target attribute, I was still getting the same result. </p>
  
  <p>Then I modified to the code to convert the target column to factor using <code>asfactor()</code> method on the H2OFrame still, there wasn't any change on the result. </p>
  
  <p>But when I changed the values in the target attribute to True and False for 1 and 0 respectively, I was getting the expected result(i.e) the output was the classification rather than the probability.</p>
  
  <ul>
  <li>What is the right way to get the classified prediction result?</li>
  <li>If probabilities are the outcomes for numerical target values, then how do I handle it in case of a multiclass classification?</li>
  </ul>