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I am relatively new to predictive modeling techniques and would like to get a few concepts cleared/discussed. I am currently in the process of building a logistic regression model using Weight of Evidence (WOE) technique. Here follows some queries/discussion topics:-

1. To start with, I would like to understand the pros and cons of using WOEs; or in other words when to use WOE over other techniques. I understand that the log odds and WOEs tend to have a linear relationship - a pre-requisite for the model. Also the WOEs make the coefficients unit free, thereby bringing them at the same level for relative inferences. In case of categorical variables, WOEs can be used to make them continuous.

But what if, the Log odds have a U-shaped relationship with the independent variable. I think the WOE will make sure of a (transformed) linear relationship but will that help? Coarse classing (combining the bins) can be an option but is it possible to completely get rid of the U-shaped relationship?

Keeping the above points in mind, can we conclude that this is a potential disadvantage of using WOE? What other techniques apart from WOEs can be used to fit logistic regression?

2. I am concerned about the interpretation of coefficients when we use WOEs instead of the original independent variables. Let us consider the following model, where the probaility of getting a heart attack is gauged through age:-

Ln(p/1-p)=A+ B(X)

Here, p=Prob(Heart attack) and X=Age

If we had used the original Age variable, then we would have interpreted the above model as :-

For unit change in age, the log odds of getting a heart attack increase by 'B'.

But if we are replacing Age with WOE(Age), will the interpretation remain the same? It can, keeping in mind the monotonic relationship between Age and WOE(Age). But I am not sure about this.

Lets discuss on the above two points as of now and we can take it forward based on your inputs.

Really appreciate for sharing your feedback,

Sayantan

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