By Trevor Hastie, Robert Tibshirani, Gareth James, Daniela Witten
An advent to Statistical studying presents an obtainable evaluation of the sector of statistical studying, a necessary toolset for making feel of the significant and complicated facts units that experience emerged in fields starting from biology to finance to advertising to astrophysics some time past 20 years. This publication offers probably the most very important modeling and prediction recommendations, in addition to correct purposes. themes comprise linear regression, type, resampling tools, shrinkage ways, tree-based equipment, aid vector machines, clustering, and extra. colour photos and real-world examples are used to demonstrate the equipment offered. because the aim of this textbook is to facilitate using those statistical studying strategies through practitioners in technological know-how, undefined, and different fields, every one bankruptcy incorporates a educational on imposing the analyses and strategies provided in R, a really renowned open resource statistical software program platform.
Two of the authors co-wrote the weather of Statistical studying (Hastie, Tibshirani and Friedman, 2d variation 2009), a favored reference publication for facts and computing device studying researchers. An creation to Statistical studying covers a few of the related issues, yet at a degree obtainable to a wider viewers. This booklet is concentrated at statisticians and non-statisticians alike who desire to use state-of-the-art statistical studying ideas to research their info. The textual content assumes just a earlier direction in linear regression and no wisdom of matrix algebra.
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Additional info for An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics, Volume 103)
It may be that with such a small number of observations, this is the best we can do. Non-parametric Methods Non-parametric methods do not make explicit assumptions about the functional form of f . Instead they seek an estimate of f that gets as close to the data points as possible without being too rough or wiggly. Such approaches can have a major advantage over parametric approaches: by avoiding the assumption of a particular functional form for f , they have the potential to accurately ﬁt a wider range of possible shapes for f .
The true f is very non-linear. There is also very little increase in variance as ﬂexibility increases. Consequently, the test MSE declines substantially before experiencing a small increase as model ﬂexibility increases. 12 is referred to as the bias-variance trade-oﬀ. Good test set performance of a statistical learning method requires low variance as well as low squared bias. This is referred to as a trade-oﬀ because it is easy to obtain a method with extremely low bias but high variance (for instance, by drawing a curve that passes through every single training observation) or a method with very low variance but high bias (by ﬁtting a horizontal line to the data).
This phenomenon, which may seem counterintuitive at ﬁrst glance, has to do with the potential for overﬁtting in highly ﬂexible methods. 6. 2 and throughout this book. 4 Supervised Versus Unsupervised Learning Most statistical learning problems fall into one of two categories: supervised or unsupervised. The examples that we have discussed so far in this chapter all fall into the supervised learning domain. For each observation of the predictor measurement(s) xi , i = 1, . . , n there is an associated response measurement yi .