Regression Modeling Strategies
by Frank E. Jr. Harrell · first published 2001
89% recommend11% neutral
Why HN recommends it
A practical, in-depth guide to regression modeling covering how models work, common pitfalls, and interpreting estimates, for data scientists and statisticians who want to move past the basics.
Summarised by Claude from recommending comments
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For regression I really like Frank Harrell's Regression Modeling Strategies. http://biostat.mc.vanderbilt.edu/wiki/Main/RmS
on “Ask HN: What are the best textbooks in your field of expertise?” ↗I believe Frank Harrell's Regression Modeling Strategies is one of the best references for performing regression analysis. website: http://biostat.mc.vanderbilt.edu/wiki/Main/RmS Amazon book: http://amzn.to/1UdPWOv
on “Guide to Linear Regression (2015)” ↗…feeding in the numbers and getting back a fitted model, but actually knowing how everything works, what the common issues are, how to interpret the estimates and so on. Once you've got that down, read Regression Modeling Strategies by Harrell to go really in depth. Or if you're really just interested in prediction, Hastie and Tibshirani is wonderful of course.
on “All of Statistics, by Larry Wassserman (2013) [pdf]” ↗I really like the way Harrell uses PCA to build regression analysis in Regression Modeling Strategies https://link.springer.com/book/10.1007/978-3-319-19425-7
on “Principal Component Analysis explained visually (2015)” ↗Online material on this subject is scattered, but there are good books like Regression Modeling Strategies by Frank E. Harrell and Credit Risk Scorecards by Naeem Siddiqi.
on “Using predictive analytics to reduce churn” ↗Frank Harrell demonstrates a way to use PCA to reduce dimensionality with regression modeling strategies. His Course Notes (pdf) is a good reference point for multiple strategies on regression. http://biostat.mc.vanderbilt.edu/wiki/Main/RmS
on “Principal Component Analysis Explained Visually (2015)” ↗If you are willing to do the work, Frank Harrell's Regression Modeling Strategies is a pretty good introduction to a lot of this. It's written for a very different set of problems than typical ML, but it has lots of really good advice for practical problems in data analysis and prediction…
on “Machine Learning Crash Course” ↗I first read about 10-20 observations per feature in a statistical model (not ML), including interactions between features, in Frank Harrell's "Regression Modeling Strategies," an excellent book that many coming from the CS side of modeling would benefit greatly from studying.
on “Some thoughts on machine learning with small data” ↗…(which is comprehensive but can dip into math that's way over my head at times) and H/R/W is Semiparametric Regression by Ruppert, Wand, and Carroll. I have also heard great things about Frank Harrell's Regression Modeling Strategies which uses a slightly different approach (still spline-based though), but I haven't read it. His other writing is fantastic though.
on “Everything is a linear model” ↗> Frank Harrell's Regression Modeling Strategies which uses a slightly different approach (still spline-based though), but I haven't read it. Very very good indeed, with the exception that he basically ignores compute time and efficiency. I learned…
on “Everything is a linear model” ↗Regression Modeling Strategies. You need at least some notion of what regressions and probability are all about, but if you have the basics covered, this book will take you through 80% of the journey and the rest is some googling to…
on “An Introduction to Model-Based Machine Learning” ↗…https://www.youtube.com/watch?v=BYUykHScxj8&list=PLDcUM9US4X... - Frank Harrel's Bioistatistics for biomedical reasearch: Frank Harrel is the go to guy to understand how to use data in clinical trials and diagnostics research. His book Regression Modeling strategies is a gem that every data scientist should read. This lecture series is aimed at biomedical researches, ie. people without a strong background in theoretical statistics. In the lectures he talks about…
on “Ask HN: What's the best lecture series you've ever seen?” ↗…me for a reasonable cut off. An example is people opting for artificial in semenation because of problem. I believe the cut off is above 60% because of the trade off. You can read more about it in Regression Modeling Strategies by Dr. Frank Harrell.
on “Logistic regression from scratch” ↗