The connection of the stability of the binary choice model with its discriminatory power

TL;DR

This paper explores the relationship between model stability indicators like PSI and KS and the discriminatory power of binary choice models, highlighting the importance of accounting for Gini index errors in model evaluation.

Contribution

It introduces a formula linking Gini index changes to stability measures and emphasizes the necessity of accurate Gini calculation for reliable model assessment.

Findings

Gini index should be less than the observed Gini when it changes.

Errors in Gini index calculation are unavoidable and must be considered.

The research applies broadly to tasks requiring scoring indicator error analysis.

Abstract

The key indicators of model stability are the population stability index (PSI), which uses the difference in population distribution, and the Kolmogorov-Smirnov statistic (KS) between two distributions. When deriving a binary choice model, the question arises about the real Gini index for any new model. The paper shows that when the Gini changes, the real Gini index should be less than the obtained Gini index. This type is included in the equation using a formula, and the PSI formula in KS is also included based on the scoring indicator. The error in calculating the Gini index of the equation is unavoidable, so it is necessary to always rely on the calculation formula. This type of research is suitable for a wide range of tasks where it is necessary to consider the error in scoring the indicator at any length.