Calibration and refinement for classification trees

Cited 2 time in webofscience Cited 0 time in scopus
  • Hit : 393
  • Download : 0
The calibration of forecasts for a sequence of events has an extensive literature. Since calibration does not ensure 'good' forecasts, the notion of refinement was introduced to provide a structure into which methods for comparing well-calibrated forecasters could be embedded. In this paper we apply these two concepts, calibration and refinement, to tree-structured statistical probability prediction systems by viewing predictions in terms of the expected value of a response variable given the values of a set of explanatory variables. When all of the variables are categorical, we show that, under suitable conditions, branching at the terminal node of a tree by adding another explanatory variable yields a tree with more refined predictions. (C) 1998 Elsevier Science B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
1998-07
Language
English
Article Type
Article
Keywords

EMPIRICAL PROBABILITY

Citation

JOURNAL OF STATISTICAL PLANNING AND INFERENCE, v.70, no.2, pp.241 - 254

ISSN
0378-3758
URI
http://hdl.handle.net/10203/67967
Appears in Collection
MA-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 2 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0