Application of Decision Tree in Machine Learning
Think about how you select different options within an automated telephone call. The options are essential decisions that are being made for you to get to the desired department. These decision trees are used effectively in many industry areas.
Financial institutions use decision trees. One of the fundamental use cases is in option pricing, where a binary-like decision tree is used to predict the price of an option in either a bull or bear market.
Marketers use decision trees to establish customers by type and predict whether a customer will buy a specific type of product. In the medical field, decision tree models have been designed to diagnose blood infections or even predict heart attack outcomes in chest pain patients.
Variables in the decision tree include diagnosis, treatment, and patient data. The gaming industry now uses multiple decision trees in movement recognition and facial recognition. The Microsoft Kinect platform uses this method to track body movement. The Kinect team used one million images and trained three trees. Within one day, and using a 1,000-core cluster, the decision trees were classifying specific body parts across the screen.