https://github.com/jhylin/ml2-3_boosted_trees
Machine learning series 2.3 on boosted trees
https://github.com/jhylin/ml2-3_boosted_trees
adaboost-classifier rdkit scikit-learn-python xgboost-classifier
Last synced: 4 months ago
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Machine learning series 2.3 on boosted trees
- Host: GitHub
- URL: https://github.com/jhylin/ml2-3_boosted_trees
- Owner: jhylin
- License: mit
- Created: 2024-02-16T04:58:00.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-04-01T23:04:38.000Z (about 1 year ago)
- Last Synced: 2025-09-12T23:53:50.937Z (9 months ago)
- Topics: adaboost-classifier, rdkit, scikit-learn-python, xgboost-classifier
- Language: Jupyter Notebook
- Homepage:
- Size: 400 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
This repository will hold most of the relevant files used for boosted trees. I've decided to keep the test notebooks (filenames ending with ...test_v1/v2/v3) - just to show that I've used small sections of them to test code etc. before finally reaching the final version.
Here's the link to [boosted trees](https://jhylin.github.io/Data_in_life_blog/posts/19_ML2-3_Boosted_trees/1_adaboost_xgb.html) post (updated on 30th Jan 2025 to add a safe inference feature to deal with invalid SMILES in Scikit-mol along with its preprint reference added). The HTML post version will make more sense than the Jupyter notebook version (some Quarto-specific layout formatting code in it, and apologies with the massive SMILES output in the print(X) cell... it won't look like this in the HTML webpage).
For other work on tree models:
1. [Decision tree](https://jhylin.github.io/Data_in_life_blog/posts/16_ML2-1_Decision_tree/1_data_col_prep.html) (this is only the 1st post, with links provided at the beginning of the post that'll lead to 2nd and 3rd posts)
2. [Random forest (regressor)](https://jhylin.github.io/Data_in_life_blog/posts/17_ML2-2_Random_forest/1_random_forest.html)
3. [Random forest classifier](https://jhylin.github.io/Data_in_life_blog/posts/17_ML2-2_Random_forest/2_random_forest_classifier.html)