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.

1. Introduction to Machine Learning in Python [Lesson materials; Code repository]
2. Introduction to Tree Models in Python [Lesson materials; Code repository]
3. Introduction to artificial neural networks in Python [Lesson materials; Code repository]
4. 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

We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any questions, concerns, or experience any difficulties along the way.

We'd like to ask you to familiarize yourself with our Contribution Guide and have a look at the more detailed guidelines on proper formatting, ways to render the lesson locally, and even how to write new episodes.

Please see the current list of issues for ideas for contributing to this repository. For making your contribution, we use the GitHub flow, which is nicely explained in the chapter Contributing to a Project in Pro Git by Scott Chacon. Look for the tag . This indicates that the maintainers will welcome a pull request fixing this issue.

Current maintainers of this lesson are:

• Tom Pollard (Website; GitHub)

A list of contributors to the lesson can be found in AUTHORS

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