Name: Applied predictive modeling
ISBN: 9781461468486

Applied predictive modeling

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 have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms.

What Hacker News says

wencJun 2021

* Fooled By Randomness (NN Taleb): Taleb is a complicated personality, but this book gave me a heuristic for thinking about long-tails and uncertain events that I could never have derived myself from a probability textbook.

* Designing Data Intensive Applications (M Kleppmann): Provided a first-principles approach for thinking about the design of modern large-scale data infrastructure. It's not just about assembling different technologies -- there are principles behind how data moves and transforms that transcend current technology, and DDIA is an articulation of those principles. After reading this, I began to notice general patterns in data infrastructure, which helped me quickly grasp how new technologies worked. (most are variations on the same principles)

* Introduction to Statistical Learning (James et al) and Applied Predictive Modeling (Kuhn et al). These two books gave me a grand sweep of predictive modeling methods pre-deep learning, methods which continue to be useful and applicable to a wider variety of problem contexts than AI/Deep Learning. (neural networks aren't appropriate for huge classes of problems)

* High Output Management (A Grove): oft-recommended book by former Intel CEO Andy Grove on how middle management in large corporations actually works, from promotions to meetings (as a unit of work). This was my guide to interpreting my experiences when I joined a large corporation and boy was it accurate. It gave me a language and a framework for thinking about what was happening around me. I heard this was 1 of 2 books Tobi Luetke read to understand management when he went from being a technical person to CEO of Shopify. (the other book being Cialdini's Influence). Hard Things about Hard Things (B Horowitz) is a different take that is also worth a read to understand the hidden--but intentional--managerial design of a modern tech company. These some of the very few books written by practitioners--rather than management gurus--that I've found to track pretty closely with my own real life experiences.

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olooneyOct 2018

Some of the best textbooks in statistics and machine learning:

Applied -------

Hosmer et al., Applied Logistic Regression. An exhaustive guide to the perils and pitfalls of logistic regression. Logistic regression is the power tool of interpretable statistical models, but if you don't understand it, it will take your foot off (concretely, your inferences will be wrong and your peers will laugh at you.) This book is essential. Graduate level, or perhaps advanced undergraduate, intended for STEM and social science grad students.

https://www.amazon.com/Applied-Logistic-Regression-Probabili...

Peter Christen's Data Matching. Record Linkage is a relatively niche concept, so Christen's book has no right to be as good as it is. But it covers every relevant topic in a clear, even-handed way. If you are working on a record linkage system, then there's nothing in this book you can afford not to know. Undergraduate level, but intended for industry practitioners.

https://www.amazon.com/Data-Matching-Techniques-Data-Centric...

Max Kuhn's Applied Predictive Modeling. Even if you don't use R, this is an incredibly good introduction to how predictive modeling is done in practice. Early undergraduate level.

http://appliedpredictivemodeling.com/

Theoretical -----------

The Elements of Statistical Learning. Probably the single most respected book in machine learning. Exhaustive and essential. Advanced undergraduate level.

https://www.amazon.com/Elements-Statistical-Learning-Predict...

Kevin Murphy's Machine Learning: A Probabilistic Perspective. Covers lots of the same ground as Elements but is a little easier. Undergraduate level.

https://www.amazon.com/Machine-Learning-Probabilistic-Perspe...

Taboga's Lectures on Probability Theory and Mathematical Statistics. Has the distinction of being available for free in web-friendly format at https://www.statlect.com/ . Undergraduate level for math majors.

https://www.amazon.com/Lectures-Probability-Theory-Mathemati...

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asteinbrFeb 2017

I can recommend Applied Predictive Modeling by Max Kuhn: http://appliedpredictivemodeling.com/

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_ofNov 2015

For those interesting in picking up machine learning that already know R, I recommend this book:

http://www.amazon.com/Applied-Predictive-Modeling-Max-Kuhn/d...

It's loaded with useful R snippets and practical examples.

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incisionJul 2014

* Computer Organization and Architecture [1]