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# An Introduction to Statistical Learning: With Applications in R

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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. This book presents some of the most important modeling and prediction techniqu
...more

## Get A Copy

Hardcover, 426 pages

Published
September 1st 2017
by Springer
(first published June 24th 2013)

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The book explains concepts of Statistical Learning from the very beginning. The core ideas such as bias-variance tradeoff are deeply discussed and revisited in many problems. The included R examples are particularly helpful for beginners to learn R. The book also provides a brief, but concise description of functions' parameters for many related R packages.

My professor thinks this book is a "superficial" version of The Elements of Statistical Learning, but I disagree. Yes, it may ...more

1. To

*do*it, not just read about it

2. To read it several times

3. To feel challenged but not overwhelmed by it

And 2&3 conflict.

(Most books don't have a natural

*do*-operator. How do yo ...more

Mar 18, 2018
Lord_Humungus
rated it
really liked it

Recommends it for:
beginners in statistics after their first course

Recommended to Lord_Humungus by:
myself

A very good book of statistics that you can read after your Statistics 101 course, centered on machine learning. Very clear prose, very consistent notation, and in general everything that one asks from a good statistics book. I've read 95% of it and it's very good if you don`t know much. I found the exercises quite difficult, though. I have no knowledge of algebra or calculus, so I just could't do some of them. And many things I had to believe by faith. I'm ok with faith, but ocassionally the au
...more

Pay attention to the videos by the authors that follow the chapters of the book (made for a Stanford MOOC but freely accessible on yt: https://www.r-bloggers.com/in-depth-i...). ...more

Feb 18, 2019
Joaquin Menendez
added it

Excellent book for anybody that wants to start adventuring in the marvelous world of data science

Based on my experience TA'ing statistical novices, I suspect the linear regression stuff is already too dense and rushed to help them really understand what's going on & why. They'll need a little more time on each aspect, a few more examples, a little deeper sense of why we do these things.

On the othe ...more

The format is a good balance between well-written prose and equations, which works well with my learning style.

The example datasets that are worked through are helpfully instructive because they contain sources of potential pitfalls, like multicollinearity, which are then highlighted and used as lessons for things to watch out for in your ...more

(Currently, I am too busy ...more

*need*any or all of those at once, they just help aid in understanding.

This book genuinely reinvigorated my interest in machine learning/statistical learning. I've been having a bit of a ...more

They did stress the idea that no one procedure was best at everything. The exercises were good about drilling this in.

I did have some problems with the book. The use of the equal sign for assignments (instead of <-) was somewhat off-putting. (Does that make me ...more

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