By Trevor Hastie, Robert Tibshirani, Gareth James, Daniela Witten
An advent to Statistical studying offers an obtainable evaluate of the sector of statistical studying, a vital toolset for making experience of the enormous and intricate facts units that experience emerged in fields starting from biology to finance to advertising to astrophysics some time past two decades. This e-book provides one of the most very important modeling and prediction innovations, in addition to suitable functions. subject matters comprise linear regression, class, resampling equipment, shrinkage ways, tree-based tools, help vector machines, clustering, and extra. colour images and real-world examples are used to demonstrate the tools offered. because the objective of this textbook is to facilitate using those statistical studying strategies via practitioners in technological know-how, undefined, and different fields, every one bankruptcy encompasses a educational on enforcing the analyses and strategies offered in R, an incredibly renowned open resource statistical software program platform.
Two of the authors co-wrote the weather of Statistical studying (Hastie, Tibshirani and Friedman, second variation 2009), a favored reference booklet for facts and laptop studying researchers. An advent to Statistical studying covers a number of the comparable subject matters, yet at a degree available to a wider viewers. This e-book is concentrated at statisticians and non-statisticians alike who desire to use state-of-the-art statistical studying thoughts 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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Additional info for An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics, Volume 103)
But we don’t really care how well our method predicts last week’s stock price. We instead care about how well it will predict tomorrow’s price or next month’s price. g. weight, blood pressure, height, age, family history of disease) for a number of patients, as well as information about whether each patient has diabetes. We can use these patients to train a statistical learning method to predict risk of diabetes based on clinical measurements. In practice, we want this method to accurately predict diabetes risk for future patients based on their clinical measurements.
When we overﬁt the training data, the test MSE will be very large because the supposed patterns that the method found in the training data simply don’t exist in the test data. Note that regardless of whether or not overﬁtting has occurred, we almost always expect the training MSE to be smaller than the test MSE because most statistical learning methods either directly or indirectly seek to minimize the training MSE. Overﬁtting refers speciﬁcally to the case in which a less ﬂexible model would have yielded a smaller test MSE.
Most of the statistical learning methods discussed in this book can be applied regardless of the predictor variable type, provided that any qualitative predictors are properly coded before the analysis is performed. This is discussed in Chapter 3. 2 Assessing Model Accuracy One of the key aims of this book is to introduce the reader to a wide range of statistical learning methods that extend far beyond the standard linear regression approach. Why is it necessary to introduce so many diﬀerent statistical learning approaches, rather than just a single best method?
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics, Volume 103) by Trevor Hastie, Robert Tibshirani, Gareth James, Daniela Witten