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An Introduction to statistical learning : with applications in R

Recurso electrónico / electronic resource
Registro MARC
Tag12Valor
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001  MAP20180018671
003  MAP
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008  180625s2018 usa|||| ||| ||eng d
010  ‎$a‎2013936251
020  ‎$a‎978-1-4614-7137-0
020  ‎$a‎978-1-4614-7138-7 (eBook)
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎937.42
24513‎$a‎An Introduction to statistical learning ‎$b‎: with applications in R‎$c‎Gareth James...[et al.]
250  ‎$a‎Corrected at 8th. printing 2017
260  ‎$a‎New York [etc.]‎$b‎Springer ‎$c‎2017
300  ‎$a‎440 p.
520  ‎$a‎Statistical learning refers to a vast set of tools for understanding data.These tools can be classified as supervised or unsupervised. Broadly speaking, supervised statistical learning involves building a statistical model for predicting, or estimating, an output based on one or more inputs.Problems of this nature occur in fields as diverse as business, medicine, astrophysics, and public policy. With unsupervised statistical learning, there are inputs but no supervising output; nevertheless we can learn relationships and structure from such data. To provide an illustration of some applications of statistical learning, we briefly discuss three real-world data sets that are considered in this book
650 4‎$0‎MAPA20080597733‎$a‎Modelos estadísticos
650 4‎$0‎MAPA20080602659‎$a‎Modelos econométricos
650 4‎$0‎MAPA20080610708‎$a‎Estadística de muestreo
650 4‎$0‎MAPA20080551797‎$a‎Muestreos
650 4‎$0‎MAPA20080593810‎$a‎Tablas estadísticas
650 4‎$0‎MAPA20080554156‎$a‎Ejercicios
7001 ‎$0‎MAPA20180008757‎$a‎James, Gareth
7102 ‎$0‎MAPA20180008764‎$a‎Springer