# How come variables with low information values may be statistically significant in a logistic regression?

My objective is to classify credit applicants into goods and bads. I calculated the information value of each feature as my primary dimension reduction technique.

I was concerned to see that some features that are typically very useful in this kind of problem had very low IVs (for example, the max overdue days of a person's credits). Thus, I ran two logistic regressions to see what would happen:

1. One with the features with an IV $$\geq$$ 0.02
2. One with the same features as the previous model plus the ones that are typically used in this sort of problem but had uncommonly low IVs

I was surprised to see that the features that had very low information values are statistically significant at 99% confidence and have relatively large coefficients.

My question is: why does this happen? Is this common?

• How do you define/calculate information value of each feature? Is that based only on that feature (and the outcome)? Apr 21 '20 at 1:41
• Yes, you isolate the feature and the target to calculate an IV. In general, an information value is a metric that summarizes how a feature accumulates bads and goods. I split a given feature into deciles to calculate the percentage of 0s and 1s in each bin, calculated the weight of evidence and got the IV. I followed this article. Apr 21 '20 at 1:45
• Apr 23 '20 at 4:30