python - how to explain the decision tree from scikit-learn

Question

I have two problems with understanding the result of decision tree from scikit-learn. For example, this is one of my decision trees:

My question is that how I can use the tree?

The first question is that: if a sample satisfied the condition, then it goes to the LEFT branch (if exists), otherwise it goes RIGHT. In my case, if a sample with X[7] > 63521.3984. Then the sample will go to the green box. Correct?

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 clf.predict(sample) to tell the category. Can I do that from the graph??? Many thanks.

Answer 1

The value line in each box is telling you how many samples at that node fall into each category, in order. That's why, in each box, the numbers in value add up to the number shown in sample. For instance, in your red box, 91+212+113=416. So this means if you reach this node, there were 91 data points in category 1, 212 in category 2, and 113 in category 3.

If you were going to predict the outcome for a new data point that reached that leaf in the decision tree, you would predict category 2, because that is the most common category for samples at that node.