title: Handle continuous variables in sklearn.tree.DecisionTreeClassifier
body: <p>I am curious how <code>sklearn</code> handles continuous variables in <code>sklearn.tree.DecisionTreeClassifier</code>? I tried to use some continuous variables without preprocessing with <code>DecisionTreeClassifier</code>, but it got an acceptable accuracy.</p>
  
  <p>Below is a kind of way to translate continuous variables into categorical variables, but it can't receive the same accuracy.</p>
  
  <pre><code>def preprocess(data, min_d, max_d, bin_size=3):
  
      norm_data = np.clip((data - min_d) / (max_d - min_d), 0, 1)
      categorical_data = np.floor(bin_size*norm_data).astype(int)
      return categorical_data
  
  
  X = preprocess(X, X.min(), X.max(), 3)
  </code></pre>