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AMIA Jt Summits Transl Sci Proc. 2012; 2012: 39–46.
Published online 2012 March 19.
PMCID: PMC3392066

I-spline Smoothing for Calibrating Predictive Models

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