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AMIA Summits Transl Sci Proc. 2012; 2012: 39–46.
Published online Mar 19, 2012.
PMCID: PMC3392066
I-spline Smoothing for Calibrating Predictive Models
Yuan Wu,* Xiaoqian Jiang,* Jihoon Kim, and Lucila Ohno-Machado1
Division of Biomedical Informatics, University of California at San Diego, La Jolla, California 92093
1 *These authors contributed equally to this manuscript.
Abstract
We proposed the I-spline Smoothing approach for calibrating predictive models by solving a nonlinear monotone regression problem. We took advantage of I-spline properties to obtain globally optimal solutions while keeping the computational cost low. Numerical studies based on three data sets showed the empirical evidences of I-spline Smoothing in improving calibration (i.e.,1.6x, 1.4x, and 1.4x on the three datasets compared to the average of competitors-Binning, Platt Scaling, Isotonic Regression, Monotone Spline Smoothing, Smooth Isotonic Regression) without deterioration of discrimination.
Articles from AMIA Summits on Translational Science Proceedings are provided here courtesy of
American Medical Informatics Association