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machine-learning-trees-python

Introduction to tree models with Python

https://github.com/carpentries-incubator/machine-learning-trees-python

Science Score: 18.0%

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Keywords

bagging boosting boostrapping decision-tree decision-trees english ensemble-model lesson machine-learning pre-alpha prediction python tree-models trees
Last synced: 6 months ago · JSON representation ·

Repository

Introduction to tree models with Python

Basic Info
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  • Stars: 3
  • Watchers: 4
  • Forks: 6
  • Open Issues: 22
  • Releases: 0
Topics
bagging boosting boostrapping decision-tree decision-trees english ensemble-model lesson machine-learning pre-alpha prediction python tree-models trees
Created over 4 years ago · Last pushed 10 months ago
Metadata Files
Readme Contributing License Code of conduct Citation Authors

README.md

Introduction to Tree Models in Python

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]

Workshop schedule

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/2022-05-24ed-dashmachine-learning/. For a list of future lessons, see: https://edcarp.github.io/Ed-DaSH/workshops

Contributing

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 good_first_issue. This indicates that the maintainers will welcome a pull request fixing this issue.

Maintainer(s)

Current maintainers of this lesson are:

Authors

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

Citation

To cite this lesson, please consult with CITATION

Owner

  • Name: carpentries-incubator
  • Login: carpentries-incubator
  • Kind: organization

Citation (CITATION) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Pollard"
  given-names: "Tom"
  orcid: "https://orcid.org/0000-0002-5676-7898"
- family-names: "Peru"
  given-names: "Giacomo"
- family-names: "Johnson"
  given-names: "Alistair"
  orcid: "https://orcid.org/0000-0002-8735-3014"
title: "Introduction to Tree Models in Python (Carpentries Incubator)"
version: 0.1.0
date-released: 2022-05-24
url: "https://github.com/carpentries-incubator/machine-learning-trees-python"

GitHub Events

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  • Issues event: 2
  • Fork event: 1
Last Year
  • Issues event: 2
  • Fork event: 1

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Last synced: 6 months ago

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  • Total issues: 23
  • Total pull requests: 9
  • Average time to close issues: about 5 hours
  • Average time to close pull requests: about 2 hours
  • Total issue authors: 6
  • Total pull request authors: 4
  • Average comments per issue: 0.43
  • Average comments per pull request: 0.56
  • Merged pull requests: 9
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
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  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 0
  • Average comments per issue: 0.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
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Top Authors
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  • tompollard (17)
  • MelanieIStefan (2)
  • alanocallaghan (1)
  • DimmestP (1)
  • milanmlft (1)
  • tobyhodges (1)
Pull Request Authors
  • tompollard (6)
  • lauracmurphy (2)
  • zkamvar (1)
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Dependencies

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