title: Re-fit a decision tree to add a layer
body: <p>I am wondering if there is a way to re-use an existing DecisionTreeClassifier to add another layer.</p>
  
  <p>My scenario is the following:</p>
  
  <ul>
  <li>I have some data (data array <strong>X</strong> with corresponding labels <strong>y</strong>) for classification</li>
  <li>I train a <strong>DecisionTreeClassifier</strong> with <code>max_depth=1</code> on my (X,y) data.</li>
  <li>While I am not satisfied with my classifier:
  
  <ul>
  <li>Re-use the same classifier with the same data, to add leaves at the bottom of my tree, therefore increasing the depth by 1 (It's important that I keep the same tree, I am just adding a layer of leaves at the bottom, I am not retraining a new DecisionTree with <code>max_depth=n+1</code>)</li>
  </ul></li>
  </ul>
  
  <p>Is there a way to do this with scikit-learn? Or with other classifiers, like a neural network, can you train your neural network with, say, 10 epochs, then if it is not accurate enough, re-train it with another 10 epochs, and so on?</p>