Speaker：Professor Pei-Ting Chou (Department of Statistics, NCCU)
Topic：美國職棒大聯盟投手的類別探索型資料分析
Speaker：Professor Pei-Ting Chou (Department of Statistics, NCCU)
Date Time：FRI. Apr 16, 2021, 10:40 AM - 11:30 AM
Place： 4F-427, Assembly Building I
Abstract
From two coupled Multiclass Classification (MCC) and Response Manifold Analytics (RMA) perspectives, we develop Categorical Exploratory Data Analysis (CEDA) on PITCHf/x database for the information content of Major League Baseball's (MLB) pitching dynamics. MCC and RMA information contents are represented by one collection of multi-scales pattern categories from mixing geometries and one collection of global-to-local geometric localities from response-covariate manifolds, respectively. These collectives shed light on the pitching dynamics and maps out the uncertainty of popular machine learning approaches. In the first part of the talk, I will talk about an indirect-distance-measure-based label embedding tree that leads to discovering asymmetry of mixing geometries among labels' point-clouds on the MCC setting. In the second part of the talk, using the CEDA approach to evaluate the reliability or uncertainty of all identifiable patterns in an extreme-K categorical sample problem will be demonstrated.