ModelOriented / ModelOriented/DALEX
Adding partial derivative and partial log-derivative
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- Dominant language
- Python
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Description
I think it could useful a function to calculate numerically not just the partial plot of the predictor, like in the function variable_effect_partial_dependency , but also the partial derivative of the predictor for the additive effect:
and the logarithmic partial derivative for the multiplicative effect:
It could be possible to calculate it like tha partial response in the variable_effect_partial_dependency function: fixing all other covariates and changing just X_j.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
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- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the linked variable_effect_partial_dependency function and the surrounding model-interpretability API. Define how numerical partial and logarithmic partial derivatives should be exposed, including edge cases for finite differences and logarithms, then add tests showing that changing only X_j produces the expected results.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100