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用于校准预测模型的I样条平滑法

I-spline Smoothing for Calibrating Predictive Models.

作者信息

Wu Yuan, Jiang Xiaoqian, Kim Jihoon, Ohno-Machado Lucila

机构信息

Division of Biomedical Informatics, University of California at San Diego, La Jolla, California 92093.

出版信息

AMIA Jt Summits Transl Sci Proc. 2012;2012:39-46. Epub 2012 Mar 19.

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.

摘要

我们提出了I样条平滑方法,通过解决非线性单调回归问题来校准预测模型。我们利用I样条的特性来获得全局最优解,同时保持较低的计算成本。基于三个数据集的数值研究表明,I样条平滑在改善校准方面有实证证据(即与竞争对手 - 分箱、普拉特缩放、保序回归、单调样条平滑、平滑保序回归的平均值相比,在三个数据集上分别提高了1.6倍、1.4倍和1.4倍),且不会降低区分度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ec4/3392066/d62e9118058d/39-joint_summit_c2012f1.jpg

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