title: scikit-learn RandomForestClassifier probabilistic prediction vs majority vote
body: <p>In the documentation of scikit-learn in <a href="http://scikit-learn.org/stable/modules/ensemble.html#random-forests" rel="nofollow">section 1.9.2.1</a> (excerpt is posted below), why does the implementation of random forest differ from the original paper by Breiman? As far as I'm aware, Breiman opted for a majority vote (mode) for classification and an average for regression (paper written by Liaw and Wiener, the maintainers of the original R code with citation below) when aggregating the ensembles of classifiers.</p>
  
  <ol>
  <li>Why does scikit-learn use probabilistic prediction instead of a majority vote?</li>
  <li>Is there any advantage in using probabilistic prediction?</li>
  </ol>
  
  <p>The section in question:</p>
  
  <blockquote>
    <p>In contrast to the original publication [B2001], the scikit-learn
    implementation combines classifiers by averaging their probabilistic
    prediction, instead of letting each classifier vote for a single
    class.</p>
  </blockquote>
  
  <p>Source: Liaw, A., &amp; Wiener, M. (2002). Classification and Regression by randomForest. R news, 2(3), 18-22.</p>