ModelOriented / ModelOriented/DALEX
add variable distribution to partial dependence plots
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- Dominant language
- Python
- Stars
- 1.5k
- Forks
- 172
- PR merge metrics
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Description
... as produced by model_profile either by marking off variable deciles at the bottom of each plot the way sklearn does it e.g. (https://scikit-learn.org/stable/auto_examples/inspection/plot_partial_dependence.html) or by including an entire histogram (example: https://towardsdatascience.com/prettifying-partial-density-plots-in-python-1f7216937ff).
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
Start by locating the model_profile implementation that assembles partial dependence plots, then compare its output with the linked scikit-learn example and histogram approach. Done means each relevant plot shows the variable distribution using decile markers or a histogram, with the chosen presentation applied consistently.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, scikit-learn
- Domain
- data-visualization, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100