title: how to explain the decision tree from scikit-learn
body: <p>I have two problems with understanding the result of decision tree from scikit-learn. For example, this is one of my decision trees:</p>
  
  <p><img src="https://i.stack.imgur.com/hwGVa.png" alt="enter image description here">
  My question is that how I can use the tree? </p>
  
  <p>The first question is that: if a sample satisfied the condition, then it goes to the <strong>LEFT</strong> branch (if exists), otherwise it goes <strong>RIGHT</strong>. In my case, if a sample with X[7] > 63521.3984. Then the sample will go to the green box. Correct?</p>
  
  <p>The second question is that: when a sample reaches the leaf node, how can I know which category it belongs? In this example, I have three categories to classify. In the red box, there are 91, 212, and 113 samples are satisfied the condition, respectively. But how can I decide the category? 
  I know there is a function  <strong>clf.predict(sample)</strong> to tell the category. Can I do that from the graph???
  Many thanks.</p>