This textbook considers statistical learning applications when
interest centers on the conditional distribution of a response
variable, given a set of predictors, and in the absence of a credible
model that can be specified before the data analysis begins.
Consistent with modern data analytics, it emphasizes that a proper
statistical learning data analysis depends in an integrated fashion on
sound data collection, intelligent data management, appropriate
statistical procedures, and an accessible interpretation of results.
The unifying theme is that supervised learning properly can be seen as
a form of regression analysis. Key concepts and procedures are
illustrated with a large number of real applications and their
associated code in R, with an eye toward practical implications. The
growing integration of computer science and statistics is well
represented including the occasional, but salient, tensions that
result. Throughout, there are links to the big picture. The third
edition considers significant advances in recent years, among which
are: the development of overarching, conceptual frameworks for
statistical learning; the impact of “big data” on statistical
learning; the nature and consequences of post-model selection
statistical inference; deep learning in various forms; the special
challenges to statistical inference posed by statistical learning; the
fundamental connections between data collection and data analysis;
interdisciplinary ethical and political issues surrounding the
application of algorithmic methods in a wide variety of fields, each
linked to concerns about transparency, fairness, and accuracy. This
edition features new sections on accuracy, transparency, and fairness,
as well as a new chapter on deep learning. Precursors to deep learning
get an expanded treatment. The connections between fitting and
forecasting are considered in greater depth. Discussion of the
estimation targets for algorithmic methods is revised and expanded
throughout to reflect the latest research. Resampling procedures are
emphasized. The material is written for upper undergraduate and
graduate students in the social, psychological and life sciences and
for researchers who want to apply statistical learning procedures to
scientific and policy problems.
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Produktdetaljer
ISBN
9783030401894
Publisert
2020
Utgave
3. utgave
Utgiver
Vendor
Springer
Språk
Product language
Engelsk
Format
Product format
Digital bok
Forfatter