https://github.com/jhylin/ml2-1_decision_tree
Machine learning series 2.1 on decision tree
https://github.com/jhylin/ml2-1_decision_tree
cheminformatics classification-models decision-tree-regression logistic-regression machine-learning
Last synced: about 1 year ago
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Machine learning series 2.1 on decision tree
- Host: GitHub
- URL: https://github.com/jhylin/ml2-1_decision_tree
- Owner: jhylin
- License: mit
- Created: 2023-08-21T04:08:35.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-09-09T04:59:50.000Z (over 1 year ago)
- Last Synced: 2025-01-29T02:48:30.698Z (over 1 year ago)
- Topics: cheminformatics, classification-models, decision-tree-regression, logistic-regression, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 754 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
#### **Machine learning series 2.1 - Decision tree**
Machine learning (ML) series 2 will be largely about tree models which encompass decision tree, random forest and boosted trees. I'm planning to have deeper dives into using each of these tree models on drug discovery data from ChEMBL database. I'll also attempt to save different tree models in different repositories as I work on them later.
This repository will hold most of the data and files used in ML series 2.1 for building a decision tree model. All the code used here was run in Python 3.9 venv and available in .ipynb or .qmd files depending on the preferences.
This series was presented as 3 posts with links provided below:
[Post 1](https://jhylin.github.io/Data_in_life_blog/posts/16_ML2-1_Decision_tree/1_data_col_prep.html) - data collection from ChEMBL database using web resource client in Python, with initial data preprocessing
[Post 2](https://jhylin.github.io/Data_in_life_blog/posts/16_ML2-1_Decision_tree/2_data_prep_tran.html) - more data preprocessing and transformation to reach the final dataset prior to model building
[Post 3](https://jhylin.github.io/Data_in_life_blog/posts/16_ML2-1_Decision_tree/3_model_build.html) - estimating experimental errors and building decision tree model using scikit-learn
Other way to access above posts is directly through my [blog](https://jhylin.github.io/Data_in_life_blog/).