This workshop will take place on Monday, 19th October 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 computer and have access to a reliable internet connection sufficient for video conferencing. A second monitor is highly recommended.

What prior experience do I need?

Basic Python knowledge
Understanding of fundamental classification concepts (recommended but not required).

Who should attend?

This workshop is ideal for researchers, data scientists, and professionals looking to apply classification techniques to their datasets for predictive analysis.

How can I contact the organiser with any questions?

You can email training@qcif.edu.au.

What's the cancellation policy?

Cancellations may be made up to 3 working days prior to the start of the workshop. If you do not join on the day without having cancelled beforehand, you may be blocked from attending any future QCIF training workshops.

Contact the host of Decision Trees and Ensemble Methods in Machine Learning (Oct 2026)

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