Please note that applying to this event through the registration form is not a guarantee of admission, and applicants will be alerted of their place by Monday 4th March. Please use your University emails to sign up to ensure eligibility for this event.

Date: Monday 11th March

Location: Debeye room, Plasma Institute, University of York

Cost: Free

Decision trees are a commonly used artificial technique that is used on tabular data for both classification and regress tasks. This workshop will first briefly introduce the decision tree family of artificial intelligence algorithms, and discuss when, where and why they can be used. We will then explore how to use these techniques in Python, using a variety of commonly used techniques and Python packages. The workshop will begin with the application of simple decision tree models, and will iteratively introduce new techniques to improve the quality of this model. We will also explore how to debug tree models when they may not be performing as well as expected.

The aim of the workshop is for participants to leave with an understanding of when tree models are an appropriate modelling technique to use, and to be confident in applying them to their own data. The workshop will focus on implementing practical examples on a real-world medical dataset, and will equip attendees with the knowledge and tools needed to apply the same techniques to their own data.

Recommended Audience:

Participants from any discipline who wish to begin to create predictive models in their research.

Prerequisites:

• Laptop, with Python and Jupyter Notebook installed (or access to an online Python environment)
• Basic Python knowledge