Classification and regression trees
by Leo Breiman · first published 1984
23mentions
1recommended by
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2011–2022first – last mentioned
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…with TeX markup: N.\ R.\ Draper and H.\ Smith, {\it Applied Regression Analysis,\/} John Wiley and Sons, New York, 1968.\ \ Leo Breiman, Jerome H.\ Friedman, Richard A.\ Olshen, Charles J.\ Stone, {\it Classification and Regression Trees,\/} ISBN 0-534-98054-6, Wadsworth \& Brooks/Cole, Pacific Grove, California, 1984.\ \ C.\ Radhakrishna Rao, {\it Linear Statistical Inference and Its Applications:\ \ Second Edition,\/} ISBN 0-471-70823-2, John…
on “Machine Learning Crash Course” ↗…(support vector machines) is an application of curve fitting related to discriminate analysis in classic multi-variate statistics. The connections with trees are likely much as in the Breiman, et al., Classification and Regression Trees (CART), a classic in applied statistics. Breiman was a good mathematician, probabilist, and applied statistician. Machine Learning is nothing like learning in humans or psychology. The course looks…
on “Introduction to Statistical Learning, with Applications in R” ↗…ordinary regression doesn't work very well, then I'm reluctant to strain with much more. I might be willing in some circumstances to use what L. Breiman (I respect Breiman, Loeve student) has in his Classification and Regression Trees.
on “Acquisitions accelerate as tech giants seek to build AI smarts” ↗…particular, get enough to understand that regression is a perpendicular projection and, thus, get the Pythagorean theorem again. For more on such statistics aimed at machine learning, get the Breiman CART -- Classification and Regression Trees, maybe much of the start of ML. With that much in statistics, will have seen a lot of applied probability and may be ready for the real stuff. For that, need measure theory, e.g., the first half of…
on “Math Basics for Computer Science and Machine Learning [pdf]” ↗…natural language processing, singular value decomposition, AI, ML, principal components analysis, support vector machines, collaborative filtering, cluster analysis, intuitive heuristics, neural nets, classification and regression trees (L. Breiman's CART), random forests, gradient descent, etc. Instead, I just derived some new math; I'm supposed to be able to do that; I've done it before on other practical problems of wide variety.…
on “Will Amazon Kill FedEx?” ↗…Applications of the model will in some suitable sense, rarely made clear, be close to the training data. Such fitting goes back at least to Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone, Classification and Regression Trees, ISBN 0-534-98054-6, Wadsworth & Brooks/Cole, Pacific Grove, California, 1984. not nearly new. This work is commonly called CART, and there has long been corresponding software. And CART goes back…
on “The Limitations of Deep Learning” ↗…at Northwestern -- long editor in chief of Mathematics of Operations Research. Neveu was a student of M. Loeve at Berkeley. So was the current darling of machine learning, L. Breiman, because of his Classification and Regression Trees (CART). Breiman's Probability as published by SIAM is generally easier reading than Neveu. For stochastic processes, there are several relatively different directions to go. Martingale theory is gorgeous,…
on “Berkeley offers its data science course online for free” ↗…units although the x86 processors are astoundingly fast anyway, able to manipulate the new data, (3) for what manipulations to do on the data, some tweaks of some of the work of L. Breiman and his CART -- Classification and Regression Trees, (4) fitting with S-shaped (neural) sigmoid functions, and (5) the radar, etc. engineering of autonomous vehicles. E.g., the neural networks might be able to simulate the operation of a neuron in a worm.…
on “The AI Misinformation Epidemic” ↗…like an updated course in otherwise just mostly classic multivariate statistics. I have about four feet of such on my bookshelf. Okay, have some more recent versions of regression and Breiman's work on classification and regression trees and random forests and some resampling. Right. Okay. Fine. Maybe as good as multivariate statistics has been for decades, maybe a little better. Fine. They are using R instead of, say, SPSS, SAS,…
on “Introduction to Statistical Learning, with Applications in R” ↗…0-471-70823-2, John Wiley and Sons, New York, 1967. N. R. Draper and H. Smith, Applied Regression Analysis, John Wiley and Sons, New York, 1968. Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone, Classification and Regression Trees, ISBN 0-534-98054-6, Wadsworth & Brooks/Cole, Pacific Grove, California, 1984. There is a lesson about curve fitting: There was the ancient Greek Ptolemy who took data on the motions of the planets…
on “Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to” ↗IMHO, might look at ML as based heavily on the Leo Breiman work in random forests and classification and regression trees (CART). Breiman was a first class applied mathematician, a student of M. Loeve at Berkeley and later a professor, of, say, statistics, at Berkeley. So, one way to get valuable information is to take…
on “Andy Grove’s Warning to Silicon Valley” ↗…learning (Vladimir Naumovich Vapnik & Alexey Chervonenkis) and SVMs 4th place: • Edwin Jaynes: maximum entropy for constructing priors (borderline: 1957) Honorable mentions: • Breiman et al.'s CART (Classification and Regression Trees) algorithm (and Quinlan's C5.0 extension) • Box–Jenkins method (autoregressive moving average (ARMA) / autoregressive integrated moving average (ARIMA) to find the best fit of a time-series model to…
on “What are the most important statistical ideas of the past 50 years?” ↗…Fienberg, {\it The Analysis of Cross-Classified Data,\/} ISBN 0-262-06063-9, MIT Press, Cambridge, Massachusetts, 1979.\ \ Leo Breiman, Jerome H.\ Friedman, Richard A.\ Olshen, Charles J.\ Stone, {\it Classification and Regression Trees,\/} ISBN 0-534-98054-6, Wadsworth \& Brooks/Cole, Pacific Grove, California, 1984.\ \ R.\ B.\ Blackman and J.\ W.\ Tukey, {\it The Measurement of Power Spectra:\ \ From the Point of View of Communications Engineering,\/}…
on “Acquisitions accelerate as tech giants seek to build AI smarts” ↗…heck of a challenge but with some progress possible, e.g., as now at the ORFE Department at Princeton. And we should note that much of the AI interest in regression is based on the work of L. Breiman in Classification and Regression Trees (CART). Breiman was, IIRC "an academic probabilist"; his text Probability (one of my favorites, e.g., for measurable selection) was all based heavily on measure theory; and his work on CART started by…
on “Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence” ↗…have some specializations. I have a gut feeling that some of the computer science community took regression a bit too seriously. Sure, I really like and respect Leo Breiman and can respect his work on classification and regression trees (do have his book but never bothered to use or get the software), but I like his text Probability published by SIAM a lot more, have it, have studied it, along with Neveu, Chung, Loeve, Skorohod, etc.…
on “Foundations Machine Learning” ↗