ISL sol & ESL sol 본문
ISL sol
https://blog.princehonest.com/stat-learning/
https://blog.princehonest.com/stat-learning/
blog.princehonest.com
https://www.kaggle.com/lmorgan95/code
Liam Morgan | Discussion Contributor
28 year old Data Scientist working at Creditspring in the UK where I spend most of my time building predictive models. Currently inactive on Kaggle but hoping to return some day when I get some inspiration for project ideas :) **Recommended Books:** The
www.kaggle.com
r
https://ab-test--rad-mooncake-254f54.netlify.app/project/islr-exercises/
Exercises of the book 'Introduction to Statistical Learning with Applications in R' | Francisco Yirá
My solutions to the exercises of [ISLR](https://book.huihoo.com/introduction-to-statistical-learning/book.html), a textbook that explains the intuition behind famous ML algorithms such as Gradient Boosting, Hierarchical Clustering and Elastic Nets, and sho
ab-test--rad-mooncake-254f54.netlify.app
파이썬
https://www.lackos.xyz/itsl/
Introduction to Statistical Learning Solutions (Python) This book (authored by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani ) is an excellent introduction to the data science and machine learning feild. Particularly in developing an an
www.lackos.xyz
ESLR sol
https://yuhangzhou88.github.io/ESL_Solution/ESL-Solution/_12-Flexible-Discriminants/ex12-01/
Ex. 12.1 - A Solution Manual for ESL
Ex. 12.1 Ex. 12.1 Show that the criteria (12.25) and (12.8) are equivalent. Soln. 12.1 For (12.8), the problem (denoted as \(P_1\)) is \[\begin{eqnarray} \label{eq:12-1a} P_1: &&\min_{\beta, \beta_0}\ \ \ \frac{1}{2} \|\beta\|^2 + C\sum_{i=1}^N\xi_i \non \
yuhangzhou88.github.io
https://waxworksmath.com/Authors/G_M/Hastie/WriteUp/Weatherwax_Epstein_Hastie_Solution_Manual.pdf
'Applied > Statistical Learning' 카테고리의 다른 글
| Chapter 8 - 1 : Tree Based Methods (0) | 2024.01.17 |
|---|---|
| Chapter 7 - 2 : Support Vector Machine (0) | 2024.01.08 |
| Chapter 7 - 1 : Support Vector Machine (0) | 2024.01.08 |
| 2. Linear Regression (0) | 2023.05.05 |
| 1. Data Exploration (0) | 2023.05.05 |
