title: RandomForestRegressor: questions about output, parameters and execution time
body: <p>I have used the following code to run and evaluate a <code>RandomForestRegressor</code> model for my data:</p>
  <p>My dataset is 36 features, 1 label with around 31 million rows. The features are continuous and the labels are binary.</p>
  <p>I have the following questions:</p>
  <ol>
  <li>When I use <code>np.unique(Y_Pred)</code> it tells me <code>array([0. , 0.5, 1. ])</code>. Why am I getting 0.5 as an output? Is there a parameter I can change in the model to fix it? I don't know whether to include it as a 1 or 0. For now I've included it as a 1 (hence Y_Pred &gt; 0.45 in my code).</li>
  <li>The documentation says the most important parameters to adjust are <code>n_estimators</code> and <code>max_features</code>. For <code>n_estimators</code> what is a reasonable number? I've started at 2 because of how slow it took to run on my TPU Google Colab session (43 minutes for each tree or 86 minutes total). Should I bother increasing trees to improve accuracy? Are there any other parameters I can change to improve speed? All of my features are reasonably important so I don't want to start dropping them.</li>
  <li>Is there anything I am doing wrong that is making it slow, or anything I can do to make it faster?</li>
  </ol>
  <p>Any help would be greatly appreciated.</p>