Comments (1)
I forgot about the use_l1
option which existed to turn this off. However to allow better control in 806ade5 I changed this option to be l1_reg
so user's can now control if they want l1_reg="bic"
, l1_reg="aic"
or a number l1_reg=2.3
. By default l1_reg="auto"
turns on lasso feature selection only when the space of possible feature masks is not well sampled (< 20%).
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Related Issues (20)
- BUG: posx and posy should be finite values HOT 1
- BUG: Shap_values have negative and possitive values; Nevertheless, all graphs have blue dots HOT 3
- BUG: Balanced RF not working for scatter plot
- BUG: PermutationExplainer does not expose `output_names`
- ENH: Keep row index in explanations of DataFrames
- ENH: Fit the model on entire dataset, vs doing a train/test split HOT 1
- BUG: interaction values for catboost regressor cannot be calculated
- ENH: increase transparency of background dataset sub-sampling HOT 4
- BUG: warnings
- BUG: Backward hook with shap.DeepExplainer on simple models with PyTorch HOT 1
- [Meta issue] Release 0.44.1 HOT 2
- shap installation failed; python version- 3.12.1 HOT 2
- MAINT: notebooks job intermittently failing on CI HOT 3
- ENH: Add Named Entity Recognition (NER) HOT 1
- AttributeError: 'Sequential' object has no attribute 'predict' HOT 1
- BUG: Python kernel crashes at importing SHAP
- Provided model function fails when applied to the provided data set.BUG: HOT 2
- BUG: AttributeError when using CatBoost with columns containing special characters
- BUG: Pytorch DeepExplainer SHAP explanations do not sum up to the model's output HOT 2
- BUG:
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