An Introduction to statistical learning : with applications in R

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<title>Introduction to statistical learning</title>
<subTitle>: with applications in R</subTitle>
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<name type="personal" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20180008757">
<namePart>James, Gareth</namePart>
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<publisher>Springer</publisher>
<dateIssued>2017</dateIssued>
<edition>Corrected at 8th. printing 2017</edition>
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<abstract displayLabel="Summary">Statistical 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</abstract>
<note type="statement of responsibility">Gareth James...[et al.]</note>
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<topic>Modelos estadísticos</topic>
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<topic>Modelos econométricos</topic>
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<topic>Estadística de muestreo</topic>
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<topic>Tablas estadísticas</topic>
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<topic>Ejercicios</topic>
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<identifier type="isbn">978-1-4614-7137-0 </identifier>
<identifier type="isbn">978-1-4614-7138-7 (eBook)</identifier>
<identifier type="lccn">2013936251</identifier>
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