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

An advent to Statistical studying presents an available evaluate of the sphere of statistical studying, an important toolset for making experience of the immense and intricate information units that experience emerged in fields starting from biology to finance to advertising to astrophysics some time past two decades. This e-book provides the most very important modeling and prediction options, in addition to proper functions. themes comprise linear regression, type, resampling tools, shrinkage methods, tree-based equipment, help vector machines, clustering, and extra. colour portraits and real-world examples are used to demonstrate the tools provided. because the aim of this textbook is to facilitate using those statistical studying strategies by means of practitioners in technological know-how, undefined, and different fields, every one bankruptcy encompasses a instructional on enforcing the analyses and techniques offered in R, a really well known 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 publication for information and desktop studying researchers. An creation to Statistical studying covers the various similar themes, yet at a degree obtainable to a much 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 information. The textual content assumes just a earlier direction in linear regression and no wisdom of matrix algebra.

**Read or Download An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics, Volume 103) PDF**

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

**Example text**

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 . In other words, we want to ﬁnd a function fˆ such that Y ≈ fˆ(X) for any observation (X, Y ). Broadly speaking, most statistical learning methods for this task can be characterized as either parametric or non-parametric. We now brieﬂy discuss these two types of approaches. Parametric Methods training data parametric nonparametric Parametric methods involve a two-step model-based approach.

Examples include a person’s age, height, or income, the value of a house, and the price of a stock. In contrast, qualitative variables take on values in one of K diﬀerent classes, or categories. Examples of qualitative variables include a person’s gender (male or female), the brand of product purchased (brand A, B, or C), whether a person defaults on a debt (yes or no), or a cancer diagnosis (Acute Myelogenous Leukemia, Acute Lymphoblastic Leukemia, or No Leukemia). We tend to refer to problems with a quantitative response as regression problems, while those involving a qualitative response are often referred to as classiﬁcation problems.

They are also somewhat less interpretable than linear regression, because the relationship between each predictor and the response is now modeled using a curve. 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.