Python H2O - prediction of Classification dataset results in numeric value

I am currently using H2O for a classification problem dataset. I am testing it out with H2ORandomForestEstimator 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).

In my data set, the target attribute is numeric i.e. True values are 1 and False values are 0. I made sure I converted the type to category for the target attribute, I was still getting the same result.

Then I modified to the code to convert the target column to factor using asfactor() method on the H2OFrame still, there wasn't any change on the result.

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.

• What is the right way to get the classified prediction result?
• If probabilities are the outcomes for numerical target values, then how do I handle it in case of a multiclass classification?

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