Introduction
This post is primarily application-oriented, so I'll skip the principles of the model. The zero-inflation negative binomial regression model is suitable for data with over-discrete and zero-inflation characteristics. For example, the number of hospitalizations and the number of disease attacks, a large number of which may never have had the event, which is called zero expansion. In problem C of the 2025 MCM competition, we need to predict which countries will win their first Olympic medal, this scenario should also use the zero-inflation model. Here I have simplified this model to a black box. Its input is a set of eigenvariables (independent variables); and a set of dependent variables, , which is the zero-bloated data that needs to be predicted. The output is the probability value of the dependent variable of 0.