Decision trees are a family of algorithms that are based around a tree-like structure of decision rules. These algorithms often perform well in tasks such as prediction and classification.
This lesson explores the properties of tree models in the context of mortality prediction. The lesson also covers topics such as overfitting, ensemble models, boosting, and bagging.
It is the second lesson in the machine learning curriculum. In later lessons we explore neural networks for image classification, and responsible machine learning.
- Introduction to Machine Learning in Python [Lesson materials; Code repository]
- Introduction to Tree Models in Python [Lesson materials; Code repository]
- Introduction to artificial neural networks in Python [Lesson materials; Code repository]
- Responsible machine learning in Python [Lesson materials; Code repository]
These lessons are being run at University of Edinburgh as part of the Ed-DaSH Data Science training programme for Health and Biosciences.
The first lessons were taught in May: https://edcarp.github.io/2022-05-24_ed-dash_machine-learning/. For a list of future lessons, see: https://edcarp.github.io/Ed-DaSH/workshops
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