By Trevor Hastie, Robert Tibshirani, Gareth James, Daniela Witten
An advent to Statistical studying offers an available evaluation of the sector of statistical studying, a necessary toolset for making experience of the massive and intricate information units that experience emerged in fields starting from biology to finance to advertising to astrophysics some time past two decades. This publication offers the most very important modeling and prediction suggestions, in addition to appropriate functions. subject matters comprise linear regression, category, resampling tools, shrinkage techniques, tree-based tools, help vector machines, clustering, and extra. colour snap shots and real-world examples are used to demonstrate the tools offered. because the aim of this textbook is to facilitate using those statistical studying concepts by way of practitioners in technology, undefined, and different fields, each one bankruptcy incorporates a educational on imposing the analyses and techniques offered 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 version 2009), a favored reference e-book for information and laptop studying researchers. An advent to Statistical studying covers a number of the similar subject matters, yet at a degree obtainable to a much wider viewers. This e-book is concentrated at statisticians and non-statisticians alike who desire to use state of the art statistical studying innovations to research their information. The textual content assumes just a past path in linear regression and no wisdom of matrix algebra.
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Additional resources for An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics, Volume 103)
Many approaches attempt to estimate the conditional distribution of Y given X, and then classify a given observation to the class with highest estimated probability. One such method is the K-nearest neighbors (KNN) classiﬁer. Given a positive integer K and a test observation x0 , the KNN classiﬁer ﬁrst identiﬁes the K points in the training data that are closest to x0 , represented by N0 . It then estimates the conditional probability for class j as the fraction of points in N0 whose response values equal j: Pr(Y = j|X = x0 ) = 1 K I(yi = j).
In this example the true f is non-linear, and so the orange linear ﬁt is not ﬂexible enough to estimate f well. The green curve has the lowest training MSE of all three methods, since it corresponds to the most ﬂexible of the three curves ﬁt in the left-hand panel. In this example, we know the true function f , and so we can also compute the test MSE over a very large test set, as a function of ﬂexibility. 9. As with the training MSE, the test MSE initially declines as the level of ﬂexibility increases.
As with the training MSE, the test MSE initially declines as the level of ﬂexibility increases. However, at some point the test MSE levels oﬀ and then starts to increase again. Consequently, the orange and green curves both have high test MSE. 9. 3), which corresponds to the lowest achievable test MSE among all possible methods. Hence, the smoothing spline represented by the blue curve is close to optimal. 9, as the ﬂexibility of the statistical learning method increases, we observe a monotone decrease in the training MSE and a U-shape in the test MSE.
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics, Volume 103) by Trevor Hastie, Robert Tibshirani, Gareth James, Daniela Witten