报告题目：Is My Binary Classifier Pretty Much the Best Possible?

报告时间：2024-03-29   10:00-11:00

报 告 人 ： 杨宇红教授  清华大学丘成桐数学科学中心

报告地点：武汉大学理学院东北楼110

Abstract：In various binary regression learning problems, classification accuracies have been persistently improving with new methods. How do we know if we are reaching the limit or there is still room for improvement? Such goodness of fit (GOF) assessment questions are important in data science and application.In this talk, we will review classical approaches to GOF assessment in binary regression and develop new methods to overcome their major drawbacks. In particular, we advocate a data splitting approach with multiple data splitting ratios to determine if a general classification algorithm (parametric or not) has a lack of fit or not for the data at hand. Simulation and real data examples show advantages of the proposed methods.This talk is based on joint works with Jie Ding, Chunling Lu and Jiawei Zhang.