> ## Documentation Index
> Fetch the complete documentation index at: https://aidflow.aigensys.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Predictor.woe_encode

**def Predictor.woe\_encode**(self, data=None, subset=None) [\[source\]](https://github.com/aigensys-labs/aidflow/blob/main/AID/\TRUENAS\aig_modeling_Products\AID\1_0_Lib\AID\predictor\predictor.py#L431)

Apply trained WOE encoding to input data.

Converts binned features into Weight of Evidence values
using the bin-level target rates locked during training.

#### Parameters

**`data`** : *`pDF | None`*
Input data (binned features). If None, returns the\
trained WOE encoding map.

**`subset`** : *`list`*
Specific features to process and return (including IXN).\
Default = None: all trained features.

#### Returns

*`WoeEncoderResult | dict`*
• data provided: data of WOE-encoded features\
• data is None: trained WOE values per bin


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