找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Elements of Data Science, Machine Learning, and Artificial Intelligence Using R; Frank Emmert-Streib,Salissou Moutari,Matthias Dehm Textbo

[复制链接]
楼主: 积聚
发表于 2025-3-26 22:11:20 | 显示全部楼层
发表于 2025-3-27 01:48:56 | 显示全部楼层
https://doi.org/10.1057/9780230510692, we discuss extended models that allow interaction terms, nonlinearities, or categorical predictors. Finally, we introduce generalized linear models (GLMs), which allow the response variable to have a distribution other than a normal distribution, thus enabling a flexible modeling of the response.
发表于 2025-3-27 07:01:25 | 显示全部楼层
https://doi.org/10.1007/978-1-349-26804-7ich is a concept introduced by Tikhonov to deal with ill-posed inverse problems. We will see that depending on the mathematical formulation of the regularization, different regression models can be derived. Perhaps the most prominent of these is the least absolute shrinkage and selection operator (LASSO) model.
发表于 2025-3-27 11:02:59 | 显示全部楼层
发表于 2025-3-27 16:45:20 | 显示全部楼层
,2.7182818284590452353602874713…,ent approaches can be used for defining clustering methods. Also, analyzing the validity of clusters can be quite intricate. However, in this chapter, we focus on clustering methods based on similarity and distance measures.
发表于 2025-3-27 21:07:50 | 显示全部楼层
发表于 2025-3-28 00:35:46 | 显示全部楼层
发表于 2025-3-28 05:20:04 | 显示全部楼层
发表于 2025-3-28 09:05:43 | 显示全部楼层
Dimension Reductiontion of the data without a significant loss of information are referred to as dimension reduction (or dimensionality reduction) techniques. In this chapter, we introduce some feature extraction and some feature selection techniques.
发表于 2025-3-28 12:12:11 | 显示全部楼层
Model Selectionon. There is a related topic called model assessment. Model selection and model assessment are frequently confused, although each of these topics focuses on a different goal. For this reason, we start our discussion about model selection by clarifying the difference compared to model assessment.
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-6-7 05:04
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表