Decision Trees: Classifying Space Objects
Actua's AI Series - Activity 2
In this activity, participants will create and evaluate decision trees. Decision trees are an approach to sorting objects or data into different types by asking questions about them. Participants will develop different questions for a decision tree by looking at an example dataset. They will then test how well their decision tree works by seeing if it can correctly label a testing dataset.
If you’re accessing this activity directly, did you know there are eight other activities in this series up on our website? These activities also follow a space exploration narrative when done in order. It is recommended to complete the activities in order but they can also be done on their own.
If you find yourself unfamiliar with any of the AI concepts and terminology introduced in these activities, please refer to our AI Glossary. For more information about Artificial Intelligence and how to incorporate it into your classroom, we suggest exploring our AI Handbook.
Here we go:
You and your group mates are astronauts and scientists aboard the Actua Orbital Station. Unfortunately, your station just got bombarded by magnetic rays and your electronics have begun to shut down! The only one who can save you is the station’s AI, DANN. DANN stands for Dedicated Actua Neural Network, and it’s gone a little loopy. Brush up on your technical skills, learn about AI, and save yourself and your crewmates!
Now that we’ve learned the basics of AI in “Introduction: What is AI?”, we can begin to fix DANN, and we can start with our scanner! The Actua Orbital Station has a large scanner to monitor and track space objects, both near and far. It looks like after the damage from the magnetic rays, the space object classifier was reset. As one of your repairs aboard the station, Mission Control has asked that you propose and evaluate a decision tree so that you can bring the space object classifier back online. Once we do that, we can move on to studying an experiment in “Regression Analysis: Making Predictions using Data”.
Educators AI Handbook
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