Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of theresponse 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 themeis to not automatically cede modeling decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Accesible discussion of statistical learning procedures for practitioners Lots of real applications discussed Intuitive explanations and visual representationof underlying statistical concepts INDICE: Statistical learning as a regression problem.- Regression splines and regression smoothers.- Classification and regression trees (CART).- Bagging.- Random forests.- Boosting.- Support vector machines.- Broader implicationsand a bit of craft lore.
- ISBN: 978-0-387-77500-5
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 370
- Fecha Publicación: 01/06/2008
- Nº Volúmenes: 1
- Idioma: Inglés