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

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 necessary toolset for making feel of the colossal and intricate info units that experience emerged in fields starting from biology to finance to advertising and marketing to astrophysics long ago two decades. This e-book provides one of the most vital modeling and prediction thoughts, in addition to appropriate purposes. themes comprise linear regression, type, resampling tools, shrinkage ways, tree-based equipment, aid vector machines, clustering, and extra. colour snap shots and real-world examples are used to demonstrate the equipment offered. because the objective of this textbook is to facilitate using those statistical studying concepts by way of practitioners in technological know-how, undefined, and different fields, each one bankruptcy encompasses a educational on imposing the analyses and strategies awarded in R, an exceptionally 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 well-liked reference e-book for facts and computer studying researchers. An creation to Statistical studying covers a few of the related subject matters, yet at a degree obtainable to a much wider viewers. This publication is focused at statisticians and non-statisticians alike who desire to use state of the art statistical studying strategies to investigate their facts. The textual content assumes just a past direction in linear regression and no wisdom of matrix algebra.

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

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These observations are called the training data because we will use these observations to train, or teach, our method how to estimate f . Let xij represent the value of the jth predictor, or input, for observation i, where i = 1, 2, . . , n and j = 1, 2, . . , p. Correspondingly, let yi represent the response variable for the ith observation. Then our training data consist of {(x1 , y1 ), (x2 , y2 ), . . , (xn , yn )} where xi = (xi1 , xi2 , . . , xip )T . Our goal is to apply a statistical learning method to the training data in order to estimate the unknown function f .

Xp change. In this situation we wish to estimate f , but our goal is not necessarily to make predictions for Y . We instead want to understand the relationship between X and Y , or more specifically, to understand how Y changes as a function of X1 , . . , Xp . Now fˆ cannot be treated as a black box, because we need to know its exact form. In this setting, one may be interested in answering the following questions: • Which predictors are associated with the response? It is often the case that only a small fraction of the available predictors are substantially associated with Y .

We discuss each in turn. Prediction In many situations, a set of inputs X are readily available, but the output Y cannot be easily obtained. 2) where fˆ represents our estimate for f , and Yˆ represents the resulting prediction for Y . In this setting, fˆ is often treated as a black box, in the sense that one is not typically concerned with the exact form of fˆ, provided that it yields accurate predictions for Y . 18 2. 3. The plot displays income as a function of years of education and seniority in the Income data set.

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