title: h2o binary classification, understand p0 and p1
body: <p>I have already read this question: <a href="https://stackoverflow.com/questions/45523997/how-should-we-interpret-the-results-of-the-h2o-predict-function">How should we interpret the results of the H2O predict function?</a>
  Still don't understand if p1 is the probability between [0,1] and could be used equally as it 's a regression and i can apply my own threshold </p>
  
  <p>edit: 
  thank you for your answer still have some confusion about it, let's dig it suppose my outcome Y is [0,1], if  Y is numeric i run it as REGRESSION and i have a single column as response. On the other hand if Y is factor run it as CLASSIFICATION and the output is: prediction/p0/p1. NOW, is p1 the same as use Y as numeric? 
  Also <a href="http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/calibrate_model.html" rel="nofollow noreferrer">http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/calibrate_model.html</a>  calibrate_model parameter affects logloss but now the max F1 is still used as threshold on P0 P1 or on the calibrated probabilities? Can i use the calibrated probabilities for regression as the logloss is supposed less?  </p>