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: Applied Statistical Learning; With Case Studies in Matthias Schonlau Textbook 2023 The Editor(s) (if applicable) and The Author(s), under e

[复制链接]
楼主: Intermediary
发表于 2025-3-25 05:41:01 | 显示全部楼层
https://doi.org/10.1007/978-3-031-33390-3Statistical Learning; Machine Learning; Stata; Applications in the Social Sciences; Case Studies; Text Da
发表于 2025-3-25 10:09:18 | 显示全部楼层
发表于 2025-3-25 15:08:17 | 显示全部楼层
Textbook 2023s of data science in the field. Although mainly intended for upper undergraduate and graduatestudents in the social sciences, given its applied nature, the book will equally appeal to readers from other disciplines, including the health sciences, statistics, engineering and computer science..
发表于 2025-3-25 18:34:58 | 显示全部楼层
发表于 2025-3-25 22:39:49 | 显示全部楼层
Statistical Learning: Concepts,f interpretation and prediction. We introduce the bias-variance tradeoff as a central theme in statistical learning. Next, we introduce the Bayes error as the lowest possible error. The Bayes error is of limited use in practice because it requires knowledge of the true functional relationship betwee
发表于 2025-3-26 03:34:05 | 显示全部楼层
Statistical Learning: Practical Aspects,cause the true functional relationship is unknown in practice, the Bayes error cannot be computed. Instead, we use different subsets of the data for training and evaluation. There are several techniques for splitting the data into subsets for this purpose. One such technique, cross-validation, uses
发表于 2025-3-26 08:12:11 | 显示全部楼层
发表于 2025-3-26 09:14:05 | 显示全部楼层
Lasso and Friends,aussian linear regression, choosing an L2 penalty leads to ridge regression and choosing an L1 penalty leads to the Lasso. The same penalties can be applied to logistic regression. Both penalties tend to reduce the magnitude of coefficients. Because the L1 penalty can reduce coefficients to zero, th
发表于 2025-3-26 14:15:31 | 显示全部楼层
发表于 2025-3-26 19:57:46 | 显示全部楼层
 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, 2025-5-3 06:34
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