An Introduction to Statistical Learning: with Applications by Trevor Hastie, Robert Tibshirani, Gareth James, Daniela

By Trevor Hastie, Robert Tibshirani, Gareth James, Daniela Witten

An advent to Statistical studying offers an obtainable evaluate of the sphere of statistical studying, a vital toolset for making experience of the big and complicated info units that experience emerged in fields starting from biology to finance to advertising to astrophysics long ago 20 years. This booklet offers essentially the most very important modeling and prediction concepts, in addition to appropriate purposes. issues contain linear regression, category, resampling equipment, shrinkage techniques, tree-based equipment, aid vector machines, clustering, and extra. colour portraits and real-world examples are used to demonstrate the equipment awarded. because the objective of this textbook is to facilitate using those statistical studying recommendations by way of practitioners in technological know-how, undefined, and different fields, each one bankruptcy includes a educational on enforcing the analyses and techniques awarded 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, second version 2009), a well-liked reference ebook for facts and computing device studying researchers. An creation to Statistical studying covers a few of the related issues, yet at a degree available to a wider viewers. This e-book is focused at statisticians and non-statisticians alike who desire to use state of the art statistical studying concepts to research their info. The textual content assumes just a prior direction in linear regression and no wisdom of matrix algebra.

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Extra resources for An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics, Volume 103)

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Finally, fully non-linear methods such as bagging, boosting, and support vector machines with non-linear kernels, discussed in Chapters 8 and 9, are highly flexible approaches that are harder to interpret. We have established that when inference is the goal, there are clear advantages to using simple and relatively inflexible statistical learning methods. In some settings, however, we are only interested in prediction, and the interpretability of the predictive model is simply not of interest. For instance, if we seek to develop an algorithm to predict the price of a stock, our sole requirement for the algorithm is that it predict accurately— interpretability is not a concern.

However, sometimes the question of whether an analysis should be considered supervised or unsupervised is less clear-cut. For instance, suppose that we have a set of n observations. For m of the observations, where m < n, we have both predictor measurements and a response measurement. For the remaining n − m observations, we have predictor measurements but no response measurement. Such a scenario can arise if the predictors can be measured relatively cheaply but the corresponding responses are much more expensive to collect.

It is not possible to fit a linear regression model, since there is no response variable to predict. In this setting, we are in some sense working blind; the situation is referred to as unsupervised because we lack a response variable that can supervise our analysis. 1 What Is Statistical Learning? 8. A clustering data set involving three groups. Each group is shown using a different colored symbol. Left: The three groups are well-separated. In this setting, a clustering approach should successfully identify the three groups.

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