How to deal with imbalanced data using logith algorithm

Question

I have an imbalanced dataset for predicting bankrutptcy using the logit algorithm. My sample has 2%(200) bankrupt firms. Unfortunately my prediction is worthless with an auroc of 0.52. On top of that using the imbalanced dataset does not even explain 1% of the dependent variable. Strikingly, using (random) balanced data prediction power is much higher.

My data has enough bankrupt firms and is a good approximation of the full population so i wonder whether or not it is wise to compensate for imbalances in my sample? And very interesting: what is the advantage and disadvantage of compensating for imbalances ?

I hope someone can help me out.

Answer 1

You could try and tune the sensitivity and specificity to increase your accuracy: This post might help! How to select a threshold for logistic regression in case of imbalance in class distribution