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Applied Predictive Modeling

4.46  ·  Rating details ·  264 ratings  ·  18 reviews
This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should ...more
Hardcover, 600 pages
Published March 30th 2018 by Springer (first published May 17th 2013)
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Bojan Tunguz
Jun 30, 2015 rated it it was amazing
“Data Science” is the most exciting research and professional fields these days. It is creating a lot of buzz, both within the academy as well as in the business world. Detractors like to point out that most of the topics and techniques used by people who call themselves Data Scientists have been around for decades if not longer. However, has often been the case that a combination of topics and methodologies becomes important and concrete enough that a truly new subfield emerges.

Predictive Mode
Jun 18, 2015 rated it it was amazing
I regard this as a more applied counterpart to more methodology oriented resources like Elements of Statistical Learning. So it applies machine learning methods that are found in readily available R libraries. In addition, the author is also the lead on the caret package in R, which provides a consistent interface between a large number of the common machine learning packages.

1. Built around case studies that are woven through the text. For each chapter, the math/stats is developed first, then t
Lee Richardson
Dec 23, 2018 rated it it was amazing
I recently went through "Data Scientist" job interviews, and some of the most common questions are related to the "process" or predictive modeling. For example:

- What would you do if there's a class imbalance?
- How would you how well your model is performing?
- What do you do if you have a lot of features, and they're correlated?!

The interviewers are essentially trying to assess if you understand the "process" of model building, and that you're resourceful enough to "know what to do" when the ana
☘Misericordia☘ ~ The Serendipity Aegis ~  ⚡ϟ⚡ϟ⚡⛈ ✺❂❤❣
An exciting book on exciting stuff.
Terran M
May 19, 2018 rated it it was amazing
I think this book is best seen as a sequel to An Introduction to Statistical Learning: With Applications in R. It has three main features:

* Practical guidance on data preprocessing, feature engineering, and handling class imbalance
* An introduction to the caret library, which offers a uniform interface to cross-validation and hyperparameter tuning
* An overview of a larger set of models and libraries than ISLR covers

Do note that the coverage of algorithms is shallower and less mathematical than I
Dec 06, 2018 rated it it was amazing
Shelves: work, read-2018
Its focus on the process of constructing and validating a predictive model is excellent.
Joshua Hruzik
Dec 02, 2017 rated it really liked it
Applied Predictive Modeling by Max Kuhn and Kjell Johnson is a complete examination of essential machine learning models with a clear focus on making numeric or factorial predictions. On nearly 600 pages, the Authors discuss all topics from data engineering, modeling, and performance evaluation.

The core of Applied Predictive Modeling consists of four distinct chapters:
1. General Strategies on how to manipulate and re-sample data.
2. Regression Models for making numeric predictions.
3. Classificati
Steven Surgnier
Apr 23, 2014 rated it it was amazing
A plethora of fantastic references with great examples of how to use caret for predictive modeling in practice.
Karsten Reuss
May 23, 2018 rated it it was amazing
Great book for those who want to learn applied data science and / or programming with R.

The book can be combined with using a R toolbox written by the authors with the identical name. It contains many interesting example datasets, too. The book is more for the advanced reader who aims at appling the techniques in practice. As a prerequisite you should have some basic programming knowledge and should have heared at least one statistics (or better chemometrics, econometrics, etc.) course. You do
Brian Peterson
Nov 02, 2016 rated it it was amazing
I work with predictive models every day, and I'm also the author of multiple R packages. This book is the best book I own on the topic of prediction. I say that even though I don't make extensive use of machine learning models, and even though there is not a single time series model in this book (when most of my work is with time series). The applied focus and wealth of practical experience on real problems is an invaluable set of insights for anyone building predictive models, in any field, and ...more
Stas Sajin
Sep 14, 2015 rated it it was amazing
This book was written by the creator of the package 'caret', which is a swiss knife of machine learning and data pre-processing algorithms. Khun covers not only a variety of stats/ML algorithms, but also delves into topics related to data preprocessing, feature selection, and Type III problems. The book also has a very detailed Computational Section, where the R code is clearly laid out. This book has a lot of great stuff.
Mar 12, 2017 rated it it was amazing
Shelves: data-science
One of the best books on predictive analytics using R. This book sets the standard for readability, usage of real life examples to illustrate concepts and thorough documentation of all R code used to create graphs and results within the book. If you're getting into analytics using R, this book is a MUST HAVE.
José Roberto
Dec 05, 2016 rated it it was amazing
A wonderful book with practical examples and suggestions for doing predictive modelling.
May 27, 2018 rated it it was amazing
Shelves: stat
Done reading it but I believe I will revisit it time and time again. Also use it as reference book.
Noah Gift
Sep 13, 2016 rated it it was amazing
I read this a few years ago and it was very helpful in doing machine learning in R. Very simple way of explaining complex topics. The primary library covered was the Caret library.
Gerrit Luimstra
Jun 29, 2020 rated it it was amazing
A very nice practical companion for people getting into machine learning and data science.
Aug 08, 2019 rated it really liked it
Shelves: probability
A useful reference for basic ML techniques.
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