Statistical Learning from a Regression Perspective considers
statistical learning applications when interest centers on the
conditional distribution of the response variable, given a set of
predictors, and when it is important to characterize how the
predictors are related to the response. As a first approximation, this
is can be seen as an extension of nonparametric regression. Among the
statistical learning procedures examined are bagging, random forests,
boosting, and support vector machines. Response variables may be
quantitative or categorical. Real applications are emphasized,
especially those with practical implications. One important theme is
the need to explicitly take into account asymmetric costs in the
fitting process. For example, in some situations false positives may
be far less costly than false negatives. Another important theme is to
not automatically cede modeling decisions to a fitting algorithm. In
many settings, subject-matter knowledge should trump formal fitting
criteria. Yet another important theme is to appreciate the limitation
of one’s data and not apply statistical learning procedures that
require more than the data can provide. The material is written for
graduate students in the social and life sciences and for researchers
who want to apply statistical learning procedures to scientific and
policy problems. Intuitive explanations and visual representations are
prominent. All of the analyses included are done in R.
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Produktdetaljer
ISBN
9780387775012
Publisert
2020
Utgiver
Vendor
Springer
Språk
Product language
Engelsk
Format
Product format
Digital bok
Forfatter