Comments (1)
Hey @mao223, thanks for showing interest in this package.
As random survival forests produce functional output it is difficult to apply the treeshap method directly, so this functionality isn't planned for now.
However, I encourage you to check out the survex package designed for explaining survival models. It implements the extension of survSHAP method for all survival models.
from treeshap.
Related Issues (20)
- Questions: Applying classification model to 'treeshap' package.. HOT 1
- ranger.unify fails on large models HOT 7
- Error: Should `unify` use the target variable?
- support categorical features HOT 2
- `max_vars` exceeding number of variables causes error in `plot_contribution`
- lightgbm.unify example erroring. HOT 2
- Feature request: consolidated unify function HOT 1
- Support for Cubist package? HOT 1
- Saabas Values HOT 1
- randomForest.unify error HOT 3
- Feature: ranger_surv.unify(type = "survival") HOT 1
- missing decision types HOT 1
- Support for other survival tree-based models HOT 3
- Release treeshap 0.3.0
- Release treeshap 0.3.1
- treeshap.model_unified fails with data.table input
- lightgbm.unify() fails when model or data are too big HOT 1
- Error in ranger_surv.unify when type= "chf"
- Error in ranger.unify() HOT 1
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from treeshap.