Abstract

Deep learning neural network models such as multilayer perceptron (MLP) and convolutional neural network (CNN) are novel and attractive artificial intelligence computing tools. However, evaluation of the performance of these methods is not readily available for practitioners yet. We provide a tutorial for evaluating classification accuracy for various state-of-the-art learning approaches, including familiar shallow and deep learning methods. For qualitative response variables with more than two categories, many traditional accuracy measures such as sensitivity, specificity, and area under the receiver operating characteristic curve are not applicable and we have to consider their extensions properly. In this paper, a few important statistical concepts for multicategory classification accuracy are reviewed and their utilities for various learning algorithms are demonstrated with real medical examples. We offer problem-based R code to illustrate how to perform these statistical computations step by step. We expect that such analysis tools will become more.

Introduction

栗家量教授现任教于新加坡国立大学，于2001年毕业于中国科学技术大学(University of Science and Technology of China)获得理学学士学位；2006年在美国威斯康星大学-麦迪逊分校(Universityof Wisconsin, Madison)获得统计学博士学位。目前研究兴趣是半参数回归分析、纵向数据、高维数据、医疗诊断、生存分析，已发表包含J Annals of Statistics, Journal of the American Statistical Association, Journal of the Royal Statistical Society Series B, Juournal of Economentrics等顶级期刊在内的论文60余篇，目前担任Biometrics, Lifetime Data Analysis,Biostatistics & Epidemiology等国际权威期刊的副主编。

Lijialiang

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