Statistical Learning from a Regression Perspective
-
- Hardcover
- Taschenbuch ausgewählt
- eBook
-
Sprache:Englisch
-
Auflage:Third Edition 2020
- Third Edition 2020 77,99 € ausgewählt
- 2. Auflage 67,99 €
77,99 €
UVP
93,49 €
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
30.06.2021
Abbildungen
XXVI, 433 p. 143 illus., 107 illus. in color.
Verlag
SpringerSeitenzahl
433
Maße (L/B/H)
23,5/15,5/2,5 cm
Gewicht
692 g
Auflage
Third Edition 2020
Sprache
Englisch
ISBN
978-3-030-42923-2
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.
Noch keine Bewertungen vorhanden
Verfassen Sie die erste Bewertung zu diesem Artikel
Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.
Kurze Frage zu unserer Seite
Vielen Dank für Ihr Feedback
Wir nutzen Ihr Feedback, um unsere Produktseiten zu verbessern. Bitte haben Sie Verständnis, dass wir Ihnen keine Rückmeldung geben können. Falls Sie Kontakt mit uns aufnehmen möchten, können Sie sich aber gerne an unseren Kund*innenservice wenden.
zum Kundenservice