By Trevor Hastie, Robert Tibshirani, Gareth James, Daniela Witten
An creation to Statistical studying offers an available review of the sphere of statistical studying, a vital toolset for making feel of the titanic and intricate information units that experience emerged in fields starting from biology to finance to advertising to astrophysics some time past two decades. This ebook provides probably the most very important modeling and prediction thoughts, besides proper functions. subject matters comprise linear regression, category, resampling tools, shrinkage methods, tree-based tools, help vector machines, clustering, and extra. colour pix and real-world examples are used to demonstrate the tools provided. because the aim of this textbook is to facilitate using those statistical studying ideas by way of practitioners in technology, undefined, and different fields, each one bankruptcy features a instructional on enforcing the analyses and strategies offered 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 favored reference publication for records and computing device studying researchers. An creation to Statistical studying covers the various related subject matters, 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 innovations to investigate their facts. The textual content assumes just a earlier direction in linear regression and no wisdom of matrix algebra.
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Additional info for An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics, Volume 103)
Instead of having to estimate an entirely arbitrary p-dimensional function f (X), one only needs to estimate the p + 1 coeﬃcients β0 , β1 , . . , βp . 2. After a model has been selected, we need a procedure that uses the training data to ﬁt or train the model. 4), we need to estimate the parameters β0 , β1 , . . , βp . That is, we want to ﬁnd values of these parameters such that ﬁt train Y ≈ β0 + β1 X 1 + β2 X 2 + . . + βp X p . 4) is referred to as (ordinary) least squares, which we discuss in Chapter 3.
This is an example of overﬁtting the data, which we discussed previously. It is an undesirable situation because the ﬁt obtained will not yield accurate estimates of the response on new observations that were not part of the original training data set. We discuss methods for choosing the correct amount of smoothness in Chapter 5. Splines are discussed in Chapter 7. As we have seen, there are advantages and disadvantages to parametric and non-parametric methods for statistical learning. We explore both types of methods throughout this book.
9) Ave (I(y0 = yˆ0 )) , indicator variable training error test error where yˆ0 is the predicted class label that results from applying the classiﬁer to the test observation with predictor x0 . 9) is smallest. 9) is minimized, on average, by a very simple classiﬁer that assigns each observation to the most likely class, given its predictor values. 10) is largest. 10) is a conditional probability: it is the probability that Y = j, given the observed predictor vector x0 . This very simple classiﬁer is called the Bayes classiﬁer.
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics, Volume 103) by Trevor Hastie, Robert Tibshirani, Gareth James, Daniela Witten