Comments (2)
To avoid notes of the type no visible binding for global variable from ggplot one can add a global_vars.R
file (like this), where you add all variables used. Another option is to add all variables used by ggplot at the top of the function and give them a NULL
value. Any thoughts?
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We have sort of decided using the former solution, see https://github.com/NorskRegnesentral/shapr/blob/master/R/zzz.R.
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Related Issues (20)
- model error HOT 1
- shapr for lightGBM HOT 1
- argument "x" is missing, with no default HOT 2
- Feature classes are not identical for model and x_train. model provided: , HOT 5
- Where to input predict_model function when using custom models HOT 1
- Will the library be made availble in Python as well? HOT 3
- Adding support for TensorFlow/Keras - anticipated challenges? HOT 1
- TypeError when running examples HOT 3
- Shap loss values HOT 2
- GroupShapley in python HOT 5
- Build Fail with Mac M1 / Ventura 13.3 HOT 2
- Error during "explain" HOT 5
- explain_forecast runs unnecessarily many predictions when multiple xreg variables are present HOT 2
- `explain()` crashes when using combined approaches on categorical and mixed data. HOT 3
- Possible bug in explain_forecast without lag explanation
- Run shapr on HPC with a large size for x_explain HOT 8
- Problem in HPC+warning: chol(): given matrix is not symmetric HOT 5
- shapr on xgboost classifier: `group` must be NULL or a list HOT 1
- Feature classes extracted from the model contains NA HOT 2
- Multiclass Dataset by Applying SHAPR Package HOT 1
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