Download e-book for kindle: An Introduction to Statistical Learning: with Applications by Trevor Hastie, Robert Tibshirani, Gareth James, Daniela

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

ISBN-10: 1461471389

ISBN-13: 9781461471387

An advent to Statistical studying presents an obtainable assessment of the sector of statistical studying, an important toolset for making feel of the gigantic and intricate facts units that experience emerged in fields starting from biology to finance to advertising and marketing to astrophysics long ago 20 years. This ebook offers probably the most very important modeling and prediction thoughts, besides proper purposes. themes contain linear regression, category, resampling tools, shrinkage techniques, tree-based tools, help vector machines, clustering, and extra. colour pictures and real-world examples are used to demonstrate the tools provided. 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 incorporates a instructional on imposing the analyses and strategies awarded 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 well-liked reference booklet for information and computer studying researchers. An advent to Statistical studying covers some of the similar issues, yet at a degree obtainable to a much wider viewers. This booklet is focused at statisticians and non-statisticians alike who desire to use state-of-the-art statistical studying ideas to investigate their information. The textual content assumes just a prior path 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)

Example text

The orange background grid indicates the region in which a test observation will be assigned to the orange class, and the blue background grid indicates the region in which a test observation will be assigned to the blue class. 5, and class two otherwise. 13 provides an example using a simulated data set in a twodimensional space consisting of predictors X1 and X2 . The orange and blue circles correspond to training observations that belong to two different classes. For each value of X1 and X2 , there is a different probability of the response being orange or blue.

This is an example of modeling for inference. Finally, some modeling could be conducted both for prediction and inference. For example, in a real estate setting, one may seek to relate values of homes to inputs such as crime rate, zoning, distance from a river, air quality, schools, income level of community, size of houses, and so forth. In this case one might be interested in how the individual input variables affect the prices—that is, how much extra will a house be worth if it has a view of the river?

The accuracy of Yˆ as a prediction for Y depends on two quantities, which we will call the reducible error and the irreducible error. In general, fˆ will not be a perfect estimate for f , and this inaccuracy will introduce some error. This error is reducible because we can potentially improve the accuracy of fˆ by using the most appropriate statistical learning technique to estimate f . However, even if it were possible to form a perfect estimate for f , so that our estimated response took the form Yˆ = f (X), our prediction would still have some error in it!

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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics, Volume 103) by Trevor Hastie, Robert Tibshirani, Gareth James, Daniela Witten

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