Comments (4)
Hi,
You can compute the information value for all variables in the dataset using the BinningProcess class. After fitting, retrieve information for all variables using method summary()
. See tutorial: http://gnpalencia.org/optbinning/tutorials/tutorial_binning_process_telco_churn.html and API: http://gnpalencia.org/optbinning/binning_process.html.
Regarding your second question, this is a modelling decision, but it is advisable to split the dataset to reduce overfitting.
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Can I manually adjust the bins?
from optbinning.
Hi @billy-odera,
You can manually adjust bins using parameter user_splits
and user_splits_fixed
. See an example here: http://gnpalencia.org/optbinning/tutorials/tutorial_binary.html#User-defined-split-points
If you have a particular example, or you can share your data, I can assist you if you encounter any difficulty.
from optbinning.
Thanks a lot
from optbinning.
Related Issues (20)
- Feature Request : 2D Binning when one of the features is missing. HOT 1
- RuntimeWarning: invalid value encountered in cast n_zeros = np.empty(n_bins).astype(np.int64) HOT 6
- Fast, greedy solver? HOT 2
- Shapely values on Scorecard object HOT 4
- Option to force continuous target type in BinningProcess HOT 1
- python setup.py install not completing HOT 2
- MulticlassBinningTable WoE HOT 1
- BinningProcess in Pipeline and cross-validation (GridSearchCV) HOT 4
- Extract p-value information from the BinningTable object HOT 1
- Negative values can lead to Scorecard failure HOT 2
- Error: Fixed user_splits are removed because produce pure prebins HOT 2
- Keep pandas.DataFrame index in `_transform` method HOT 3
- Get IV of each feature after applying MulticlassOptimalBinning in multiclass dataset (>2 labels) HOT 6
- Transform calculates WoE=0 for special_codes HOT 2
- WoE monotonicity prediction with ML HOT 1
- For different special values ,how to assign different metric_special value? HOT 1
- Count % on WOE plot HOT 2
- plot savefig issue HOT 1
- type_of_target are not proper to decide multiclass vs continuous HOT 2
- Randomness in the binning : Getting Different Bins each time HOT 7
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