Local Versus Global Models for Classification Problems
Authors:
- 1 May 2003
- journal article
- Published by Taylor & Francis in The American Statistician
- Vol. 57 (2) , 124-131
Abstract
It is generally argued that predictive or decision making steps in statistics are separate from the model building or inferential steps. In many problems, however, predictive accuracy matters more ...Keywords
This publication has 15 references indexed in Scilit:
- Relative Efficiencies of Kernel and Local Likelihood Density EstimatorsJournal of the Royal Statistical Society Series B: Statistical Methodology, 2002
- Modelling consumer credit riskIMA Journal of Management Mathematics, 2001
- Defining attributes for scorecard construction in credit scoringJournal of Applied Statistics, 2000
- Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)The Annals of Statistics, 2000
- Improving the Practice of Classifier Performance AssessmentNeural Computation, 2000
- Comparing classifiers when the misallocation costs are uncertainPattern Recognition, 1999
- A Decision-Theoretic Generalization of On-Line Learning and an Application to BoostingJournal of Computer and System Sciences, 1997
- On Bias, Variance, 0/1—Loss, and the Curse-of-DimensionalityData Mining and Knowledge Discovery, 1997
- Boosting a Weak Learning Algorithm by MajorityInformation and Computation, 1995
- Some Problems Connected with Statistical InferenceThe Annals of Mathematical Statistics, 1958