Assign missing class to a bin & BinningProcess with user splits

[billy-odera]

I am running the below

user_splits =  [2,6,14,24,34]
optb = OptimalBinning(name=variable, dtype="numerical", solver="mip",user_splits=user_splits)
optb.fit(x, y)
binning_table = optb.binning_table
binning_table.build()

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

[guillermo-navas-palencia]

Hi @billy-odera,

1. 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.

2. You need to pass a dictionary with particular options via parameter binning_fit_params. Example:

binning_fit_params = {
    "variable": {"dtype": "numerical", "solver": "mip", "user_splits": [2,6,14,24,34]}
}

binning_process = BinningProcess(variable_names, categorical_variables=categorical_variables, 
                                 selection_criteria=selection_criteria, binning_fit_params=binning_fit_params)

[billy-odera]

Thanks for the feedback