# Weight of Evidence is logistic coefficients

Friday September 17, 2021

“Weight of Evidence” (WoE) is a good idea for decision-making, but, especially in the financial risk modeling world, it's also a specific feature processing method based on target statistics. Weight of Evidence changes a categorical variable into the log odds coefficients corresponding to a logistic regression with intercept at the overall rate.

Say you have a categorical predictor, or a continuous predictor that you're going to bin into categories in order to make it easier to model nonlinear relationships, and a binary outcome like “defaulted on loan or not”. Then for each category, the WoE score is:

$\text{WoE} = \log \left( \frac{\text{count of positives for this category} / \text{count of all positives}} {\text{count of negatives for this category} / \text{count of all negatives}} \right)$

import numpy as np
import pandas as pd
import category_encoders as ce

X = pd.DataFrame({'x': ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b']})
y =                    [ 1 ,  1 ,  0 ,  0 ,  1 ,  0 ,  0 ,  0 ]

woe = ce.woe.WOEEncoder(regularization=0)
woe.fit(X, y)
woe.transform(pd.DataFrame({'x': ['a', 'b']}))
## (0.5108256237659906, -0.587786664902119)

np.log((2/3)/(2/5)), np.log((1/3)/(3/5))
## (0.5108256237659906, -0.587786664902119)

It's usually written like that, sometimes with additive smoothing (add some small numbers to the counts) to avoid zeros and reduce variance. To make it even clearer that this is just log odds, rearrange to:

$\text{WoE} = \log \left( \frac{\text{count of positives for this category}} {\text{count of negatives for this category}} \right) - \log \left( \frac{\text{count of all positives}} {\text{count of all negatives}} \right)$

np.log(2/2) - np.log(3/5), np.log(1/3) - np.log(3/5)
## (0.5108256237659907, -0.5877866649021191)

The WoE values are exactly the coefficients you'd get if you made indicator columns for your categorical variable, added an intercept column, and did a logistic regression. That design matrix isn't full rank, so there isn't a unique solution—unless you set the intercept coefficient to represent the overall log odds (as in the subtracted term above).

These WoE scores are monotonic with the category-specific percent positive, for example, so if you're going to use a tree-based model where spacing doesn't matter, the more immediately interpretable value might be preferable. If you're doing a simple logistic regression with a WoE predictor, the coefficient will be one. In combination with other features, it isn't obvious to me that WoE will always be an absolutely optimal transform, but it seems like a fine choice for multiple logistic regression as well. Using WoE instead of a categorical feature can prevent learning interactions with the affected categories, etc., which could be a consideration.

Like other transforms based on target statistics, WoE can leak label information into training data. Even in large datasets, if some categories appear rarely, this may be a problem. Cranking up the additive smoothing ("regularization") a bit might help, or consider alternatives as in the CatBoost paper etc.

I think WoE is interesting in part because it's sort of on the threshold between a simple pre-processing step and what might be considered model stacking. It's a reminder that even “fancy” models are just statistics with more steps; the mean is a model too.

If you're working on credit scoring, maybe you'll also do feature selection using “Information Value” (IV). I don't know... It reminds me a little bit of MIC, almost? That's not quite right. I'm not so interested in IV right now.