Decision Trees and Ensemble Methods in Machine Learning (Jun 2026)

This workshop will take place on Friday, 5th June 9:30-1:30pm AEST.

Decision trees are a fundamental supervised learning method that form the backbone of many powerful ensemble models. In this interactive online workshop, participants will learn the theory behind decision trees, including how they split data, handle overfitting, and work with both classification and regression problems. We will also explore advanced tree-based methods, including Random Forests, Gradient Boosting, and XGBoost. Through coding exercises in Python (Scikit-Learn, XGBoost), attendees will build and fine-tune decision tree models on real-world datasets. By the end of the session, participants will have a solid understanding of decision trees, their strengths and weaknesses, and when to apply ensemble learning techniques. No prior machine learning experience is required, but basic Python and statistics knowledge are recommended.

FAQs

What should I bring into the event?

This is an online workshop, so you will need to provide your own…