New Extreme Value Distribution link function for Binary Classification Model: Simulation Study

The proposed research incorporates the utilization of a heavy-tailed skewe–d distribution referred to as the inverse Weibull as a link function in the context of a binary classification model. This selection is motivated by the need to ad-dress the existence of rare or extreme events in random processes. The study introduces a model that relies on the Inverse Weibull (TYPE II) distribution, and the estimation of model parameters is accomplished through the appli-cation of maximum likelihood measures. When the results are compared to those derived from other link functions such as TYPE I (Complementary log) and TYPE III (Weibull) based on extreme value distributions using simulation data, it becomes apparent that the Inverse Weibull (TYPE II) model exhibits exceptional performance. This performance assessment takes into account several criteria, including the Akaike information criterion, the Bayesian in-formation criterion, the area under the curve, and the Brier scores. In conclu-sion, the study establishes that the proposed model demonstrates considerable robustness in its performance, rendering it a viable choice for the modeling of binary classification problems.