講  題:Categorical Exploratory Data Analysis in Major League Baseball

          主講人:周珮婷 助理教授(國立政治大學統計學系)

     時  間:20211104日(星期四)下午0210 - 0400

     地  點:B302A(淡水校園商管大樓)

     茶  會:20211104日(星期四)下午0130 (商管大樓 B1102) 

         *演講以實體方式進行(視疫情滾動式調整)

 

 

 摘 要

 

      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 discuss an indirect-distance-measure-based label embedding tree that leads to discovering the 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.


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更新日期 : 2022/05/20