Comments (2)
For future travellers: I've found that using example.final_cfs_df_sparse
returns the desired precision in the predictions. I haven't found a way to keep the original precision in the "instance" df
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As for the "instance" dataframe, my best solution was to simply use the original dataframe passed to DiCE rather than use it's output. As for the predicted target value, simply do prediction with your model on your "instance" data.
from dice.
Related Issues (20)
- Cannot perform DataFrame operations on generated counterfactuals HOT 2
- ('Feature', ... , 'has a value outside the dataset.') caused by type mismatch HOT 3
- show(shap_local) in .py file
- TypeError: _generate_counterfactuals() got an unexpected keyword argument 'feature_weights' HOT 3
- "ValueError: DataFrame.dtypes for data must be int, float or bool. Did not expect the data types in fields" even for the columns with type as HOT 1
- How to generate CF for three-dimensional dataset
- Unexpected Behavior in Calculating ”feature_weight_list“ leads to abnormal loss?
- Sometimes Counterfactuals generated with random method have wrong class HOT 1
- TypeError: expected str, bytes or os.PathLike object, not CatBoostRegressor HOT 3
- pandas > 2.0.0 should be supported
- Error when opening the notebook "DiCE_getting_started_feasible.ipynb".
- Permitted range
- Dice Object Initialization Error
- Desired output is 1 and query is the one which has the original output 0. How to select such queries?
- DiCE for Custom Model Input
- Can't import dice_ml because of raiutils lib HOT 2
- Factual presented in explanation is different from original factual HOT 1
- AttributeError: 'PrivateData' object has no attribute 'data_df'
- DiCE_getting_started_feasible notebook typo resulting in failure to render
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from dice.