Comments (1)
There's no such functionality in this package but you can use black-box prediction explainers from other libraries based on e.g. SHAP or local sensitivity.
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Related Issues (20)
- isolation.forest() is not reproducible whenever `nthreads > 1` HOT 13
- Error in fit_model(pdata$X_num, pdata$X_cat, unname(pdata$ncat), pdata$Xc, : std::bad_alloc HOT 3
- De-serialized Tree estimators in R HOT 1
- module 'numpy.random' has no attribute 'Generator' HOT 1
- Models without `categ_cols` fail to export HOT 3
- Not able to run a loop in parallel using joblib HOT 2
- Problem with saving trained isolation forest when `categ_cols` is not None. HOT 6
- Difficulty installing Isotree for R on Linux HOT 7
- Question: When categorical attributes are split into subsets, are the subsets random? HOT 2
- What is equivalent to contamination parameter in sklearn? HOT 1
- Mac arm nthreads problem HOT 1
- Error when using fit() and build_imputer=True is misleading HOT 2
- Same category is always imputed when enough trees are grown HOT 3
- pip install issue: cc1plus no such file or directory HOT 5
- pip install issue not fixed but closed HOT 1
- Reproductibility problems with Extented Isolation Forest HOT 8
- How to re-train when a few values are marked as anomalies when they should not have been? HOT 3
- Isotree Isolation forest generating large model pickle file HOT 3
- Training Isotree with Python on Windows then Deserializing with C++ on Raspberry Pi 3B+ (Linux) HOT 7
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