论文标题
一种可视化和优化统计和机器学习模型的预测概况时控制外推的方法
A Method for Controlling Extrapolation when Visualizing and Optimizing the Prediction Profiles of Statistical and Machine Learning Models
论文作者
论文摘要
我们提出了一种在JMP软件中预测剖面中控制外推的新方法。预测剖面是用于探索统计和机器学习模型高维预测表面的图形工具。 Profiler包含模型预测表面的交互式横截面视图或轮廓痕迹。我们的方法可帮助用户避免探索应该被视为外推的预测。它还在约束因子区域进行优化，该区域避免使用遗传算法外推。在模拟和现实世界中，我们演示了经常外推没有约束的最佳因素设置，以及推出控制如何帮助避免使用可能对用户无用的无效因素设置避免使用这些解决方案。
We present a novel method for controlling extrapolation in the prediction profiler in the JMP software. The prediction profiler is a graphical tool for exploring high dimensional prediction surfaces for statistical and machine learning models. The profiler contains interactive cross-sectional views, or profile traces, of the prediction surface of a model. Our method helps users avoid exploring predictions that should be considered extrapolation. It also performs optimization over a constrained factor region that avoids extrapolation using a genetic algorithm. In simulations and real world examples, we demonstrate how optimal factor settings without constraint in the profiler are frequently extrapolated, and how extrapolation control helps avoid these solutions with invalid factor settings that may not be useful to the user.
