pymc-devs / pymc-devs/pymc-bart
Getting a frequentist model from BART model
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- Dominant language
- Python
- Stars
- 156
- Forks
- 32
- Avg merge
- 15h 6m
- Merged PRs (30d)
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Description
Short Description
Hi, I have been wordering if there is a way to connect the bart regression model with some other tree based model, like XGboost or sklearn's trees. This in order to use the bart model to estimate some of the parameters of these models and then use the other tree models to generate SHAP values. This is based on the following blog in towardsdatascience.com: Bayesian inference.
This will generate a more thorough analysis of the black box model generated from the BART model.
Code Example or link
This can be seen in examples from : this blog post
And the shap value package is: shap
Relevant documentation or public examples
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No repository file, test, or entry point is identified. Start with the linked blog post and SHAP documentation, then inspect the repository’s BART usage examples; a finished issue would need a defined integration path and tests demonstrating the requested model and SHAP workflow.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100