statistical learning has become a critical toolkit for anyone who wishes to understand data.An Introduction to Statistical Learningprovides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data...
An Introduction to Statistical Learning with Applications in R - Gareth J.et al. Python Machine Learning- Sebastian Raschka Programming Collective Intelligence (集体编程智慧) - Toby Segaran 机器学习 - 周志华 统计学习方法 - 李航 最近我阅读了上面的书籍,想和大家分享一下我的主观评价。在每本书的总评...
本文参考书籍《An Introduction to Statistical Learning》[1] 2.1、什么是统计学习? 为了激发大家对统计学习的研究,我们从一个简单的例子开始。 假设我们是一位客户雇用的统计顾问,以提供有关如何提高特定产品销售的建议。我们现在得到了一份广告数据集,其中包括该产品在200个不同市场中的销售数据,以及在每个市场中针...
【英语一小时】译读 An Introduction to Statistical Learning(1) 小善乄 【英语一小时】译读 An Introduction to Statistical Learning(5) 小善乄 小善乄 1:06:48 【英语一小时】译读 An Introduction to Statistical Learning(2) 小善乄 【英语一小时】译读 An Introduction to Statistical Learning(3) ...
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. Th...
An Introduction to Statistical Learning,2nd editio 电子书 阅可堂文学专营店 关注店铺 评分详细 商品评价: 4.7 高 物流履约: 3.8 低 售后服务: 4.8 高 手机下单 进店逛逛|关注店铺 关注 企业购更优惠 An Introduction to Statistical Learning,2nd editio 电子书 ...
Title: An Introduction to Statistical Learning: with Applications in R, 2nd Edition Author(s) Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Publisher: Springer; 2nd ed. (2021); eBook (Corrected Edition, June 21, 2023) Hardcover: 622 pages eBook: PDF (615 pages) Language:...
"An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement ...
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我们首先介绍简单线性回归的概念。它假设自变量与因变量之间存在线性关系，并通过最小均方误差（MSE）方法，找到最优斜率与截距，以最小化预测值与实际值的平方误差之和。最小二乘法是求解线性回归参数的常见方法，其优化目标是找到一组斜率和截距，使得函数输出与实际值之间的欧氏距离之和最小。通过求偏...