https://github.com/hsma-programme/h6_4e_boosted_trees
https://github.com/hsma-programme/h6_4e_boosted_trees
Last synced: 2 months ago
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- Host: GitHub
- URL: https://github.com/hsma-programme/h6_4e_boosted_trees
- Owner: hsma-programme
- License: other
- Created: 2024-04-29T09:19:39.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-08-02T13:31:55.000Z (9 months ago)
- Last Synced: 2024-08-02T15:09:11.947Z (9 months ago)
- Language: Jupyter Notebook
- Size: 8.18 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## Slides
## Lecture Recording
Boosted Trees for Classification: 
Boosted Trees for Regression:
## Exercises
The notebooks in the `exercises` folder can be downloaded and run locally if you have Python installed.
Alternatively, you can run each exercise on **Google Colab**, a free online platform for coding exercises. You will need to be logged in to a google account in your browser.
Using the links below will open a fresh copy of the notebook to work on - your changes will not be visible to anyone else. However, if you want to be able to refer back to your version of the notebook in future, make sure you click **'File --> Save to Drive'**.
Your changes will then be saved to your own account, and you can access your edited copy of the notebook from https://colab.research.google.com/.
Open Exercise 1 in Google Colab:
Open Exercise 2 in Google Colab:
### Exercise Structure
Notebooks are split into **core**, **extension** and **challenge** sections.
All students should aim to complete the exercises within the **core** section. Completing these exercises will give you practice of all of the key concepts discussed in the lectures and you can stop after this section if you wish.
Students looking to push themselves and their understanding can go on to attempt the **extension** exercises if they would like to.
The **challenge** section contains exercises that may go beyond what is covered in the lectures; there will be an expectation of looking things up in documentation or on sites such as StackOverflow, or using tools such as perplexity.ai to obtain boilerplate code. These exercises may take significantly longer than is allocated during the lectures and are designed to be an enjoyable challenge for those who want to push their coding skills.
## Solutions
Coming Soon.