title: RandomForestClassifier vs ExtraTreesClassifier in scikit learn
body: <p>Can anyone explain the difference between the RandomForestClassifier and ExtraTreesClassifier in scikit learn.  I've spent a good bit of time reading the paper:</p>
  
  <p>P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006 </p>
  
  <p>It seems these are the difference for ET:</p>
  
  <p>1) When choosing variables at a split, samples are drawn from the entire training set instead of a bootstrap sample of the training set.</p>
  
  <p>2) Splits are chosen completely at random from the range of values in the sample at each split.  </p>
  
  <p>The result from these two things are many more "leaves".</p>