Machine Learning for OpenCV

Investigating the inner workings of a decision tree

We established earlier that a decision tree is basically a flow chart that makes a series of decisions about the data. The process starts at the root node (which is the node at the very top), where we split the data into two groups, based on some decision rule. Then the process is repeated until all remaining samples have the same target label, at which point we have reached a leaf node.

In the spam filter example earlier, decisions were made by asking True/False questions. For example, we asked whether an email contained a certain word. If it did, we followed the edge labeled True and asked the next question. However, this does not just work for categorical features, but also for numerical ...