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that gives the below sample output How do I force the missing bin to be in bin 0?
After running the user splits for the specific variables, how do I then use those splits when running the binning_process = BinningProcess(variable_names, categorical_variables=categorical_variables, selection_criteria=selection_criteria) fro the scorecard
The text was updated successfully, but these errors were encountered:
It is not possible to combine the missing bin with other bins. A missing bin is automatically generated if the dataset includes missing data. However, you can change the WoE value from -0.418329 to -0.0854013 when transforming using transform function in OptimalGrouping: http://gnpalencia.org/optbinning/binning_binary.html#optbinning.OptimalBinning.transform; use metric_missing= -0.0854013.
You need to pass a dictionary with particular options via parameter binning_fit_params. Example:
I am running the below
that gives the below sample output
![image](https://user-images.githubusercontent.com/12785557/89458385-9dfa7500-d76f-11ea-95af-2e6364783d4d.png)
How do I force the missing bin to be in bin 0?
After running the user splits for the specific variables, how do I then use those splits when running the
binning_process = BinningProcess(variable_names, categorical_variables=categorical_variables, selection_criteria=selection_criteria) fro the scorecard
The text was updated successfully, but these errors were encountered: