Regression Error Characteristic Curves
Regression Error Characteristic Curves
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发表时间：
2003-08
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通讯作者：
J. Bi;Kristin P. Bennett
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作者：
J. Bi;Kristin P. Bennett
受试者工作特征（ROC）曲线为分类结果的可视化和比较提供了有力的工具。回归误差特征曲线（REC）将ROC曲线推广到回归。REC曲线在x轴上绘制误差容限与在y轴上的容限内预测的点的百分比。所得曲线估计误差的累积分布函数。REC曲线直观地呈现了常用的统计数据。曲线上面积（AOC）是对预期误差的有偏估计。R2值可以使用给定模型的AOC与空模型的AOC之比来估计。用户可以通过检查REC曲线的相对位置来快速评估许多回归函数的相对优点。曲线的形状揭示了可用于指导建模的附加信息。
Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualizing and comparing classification results. Regression Error Characteristic (REC) curves generalize ROC curves to regression. REC curves plot the error tolerance on the x-axis versus the percentage of points predicted within the tolerance on the y-axis. The resulting curve estimates the cumulative distribution function of the error. The REC curve visually presents commonly-used statistics. The area-over-the-curve (AOC) is a biased estimate of the expected error. The R2 value can be estimated using the ratio of the AOC for a given model to the AOC for the null model. Users can quickly assess the relative merits of many regression functions by examining the relative position of their REC curves. The shape of the curve reveals additional information that can be used to guide modeling.