Risk estimation using probability machines

BioData Mining

Volumn 7, Issue 1, 2014, Pages

  Dasgupta, Abhijit ^a   Szymczak, Silke ^b,e   Moore, Jason H ^c   Bailey Wilson, Joan E ^b   Malley, James D ^d  

^a NATIONAL INSTITUTE OF ARTHRITIS AND MUSCULOSKELETAL AND SKIN DISEASES   (United States)

^b NATIONAL HUMAN GENOME RESEARCH INSTITUTE   (United States)

^c DARTMOUTH HITCHCOCK MEDICAL CENTER   (United States)

^d NATIONAL INSTITUTES OF HEALTH   (United States)

^e UNIVERSITY OF KIEL   (Germany)

Author keywords

Consistent nonparametric regression; Counterfactuals; Interactions; Logistic regression; Odds ratio; Probability machine

Indexed keywords

ARTICLE; EFFECT SIZE; LOGISTIC REGRESSION ANALYSIS; MACHINE LEARNING; PREDICTION; PRIORITY JOURNAL; PROBABILITY; RANDOM FOREST; SIMULATION;

EID: 84899100296     PISSN: None     EISSN: 17560381     Source Type: Journal    
DOI: 10.1186/1756-0381-7-2     Document Type: Article

References (8)

1. Probability machines: consistent probability estimation using nonparametric learning machines. Malley JD, Kruppa J, Dasgupta A, Malley KG, Ziegler A, Methods Inf Med 2012 51 74 81 [ http://dx.doi.org/10.3414/ME00-01- 0052 ] 21915433
2. Random forests. Breiman L, Mach Learn 2001 45 5 32 10.1023/A: 1010933404324 (Pubitemid 32933532)
3. Consistency of random forests and other averaging classifiers. Biau G, Devroye L, Lugosi G, J Mach Learn Res 2008 9 2015 2033
4. On the rate of convergence of the bagged nearest neighbor estimate. Biau G, Cerou F, Guyader A, J Mach Learn Res 2010 11 687 712
5. Analysis of a random forests model. Biau G, J Machine Learning Res 2012 13 1063 1095
6. Comparative analysis of the D. melanogaster modEncode transcriptome annotation. Chen Z-X, Sturgil D, Qu J, Jiang H, Park S, Boley N, Suzuki AM, Fletcher AR, Plachetzki DC, FitzGerald PC, Artieri CG, Atallah J, Barmina O, Brown JB, Blankenburg KP, Clough E, Dasgupta A, Gubbala S, Han Y, Jayaseelan JC, Kalra D, Kim Y-A, Kovar CL, Lee SL, Li M, Malley JD, Malone JH, Mathew T, Mattiuzzo NR, Munidasa M, et al. Genome Research in press
7. Classification and regression by random forest. Liaw A, Wiener M, R News 2002 2 3 18 22
8. Scikit-learn: machine learning in python. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E, J Mach Learn Res 2011 12 2825 2830