New PDF release: An Introduction to Statistical Learning: with Applications

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

ISBN-10: 1461471389

ISBN-13: 9781461471387

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.

Show description

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

Best statistics books

Read e-book online R Graphics (Chapman & Hall/CRC The R Series) PDF

R is revolutionizing the area of statistical computing. strong, versatile, and better of all unfastened, R is now this system of selection for tens of hundreds of thousands of statisticians.
Destined to turn into an rapid vintage, R portraits offers the 1st whole, authoritative exposition at the R graphical approach. Paul Murrell, widely recognized because the major specialist on R pics, has constructed an in-depth source that takes not anything without any consideration and is helping either neophyte and professional clients grasp the intricacies of R images. After an introductory review of R snap shots amenities, the presentation first makes a speciality of the conventional photos procedure, exhibiting how one can paintings the normal capabilities, describing capabilities which are to be had to provide entire plots, and the way to customise the main points of plots.
the second one a part of the booklet describes the grid pictures procedure - a process certain to R and masses extra robust than the conventional procedure. the writer, who was once vital within the improvement of the grid process, exhibits, ranging from a clean web page, the way it can be utilized to supply graphical scenes. He additionally describes how you can increase new graphical capabilities which are effortless for others to take advantage of and construct on. Appendices comprise a short advent to the R method often and speak about how the conventional and grid pix platforms could be combined.
a lot of the knowledge provided during this booklet can't be discovered anyplace else. good sooner than the curve, relatively in regards to the grid procedure, R pix may have a big effect at the destiny course of statistical pictures development.
the writer continues an internet site with additional info.

Download e-book for iPad: Statistical Learning and Data Science (Computer Science & by Fionn Murtagh, Mireille Gettler Summa, Leon Bottou, Bernard

Data research is altering speedy. pushed by means of an unlimited variety of software domain names and reasonable instruments, computer studying has turn into mainstream. Unsupervised facts research, together with cluster research, issue research, and occasional dimensionality mapping equipment continuously being up-to-date, have reached new heights of accomplishment within the enormously wealthy info global that we inhabit.

Statistical studying and information technology is a piece of reference within the speedily evolving context of converging methodologies. It gathers contributions from a few of the foundational thinkers within the assorted fields of information research to the most important theoretical ends up in the area. at the methodological entrance, the quantity comprises conformal prediction and frameworks for assessing self belief in outputs, including attendant threat. It illustrates a variety of purposes, together with semantics, credits possibility, power creation, genomics, and ecology. The ebook additionally addresses problems with starting place and evolutions within the unsupervised facts research enviornment, and offers a few techniques for time sequence, symbolic facts, and sensible data.

Over the background of multidimensional information research, an increasing number of complicated facts became to be had for processing. Supervised computer studying, semi-supervised research methods, and unsupervised information research, offer nice strength for addressing the electronic info deluge. Exploring the principles and up to date breakthroughs within the box, Statistical studying and information technology demonstrates how information research can enhance own and collective wellbeing and fitness and the healthiness of our social, company, and actual environments.

Download e-book for iPad: Basics of Modern Mathematical Statistics (Springer Texts in by Vladimir Spokoiny, Thorsten Dickhaus

This textbook offers a unified and self-contained presentation of the most methods to and ideas of mathematical information. It collects the fundamental mathematical principles and instruments wanted as a foundation for extra severe reviews or maybe self reliant study in statistics. the vast majority of present textbooks in mathematical data stick with the classical asymptotic framework.

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

Sample text

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?

Download PDF sample

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


by Kevin
4.4

Rated 4.43 of 5 – based on 21 votes