统计和机器学习模型预测曲线可视化和优化时控制外推的方法
A Method for Controlling Extrapolation when Visualizing and Optimizing the Prediction Profiles of Statistical and Machine Learning Models
摘要：
提出了一种新的JMP软件中预测刻画器外推控制方法。预测刻画器是一个图形化工具，用于探索统计和机器学习模型的高维预测曲面。刻画器包含模型预测曲面的交互式横截面视图或轮廓跟踪。我们的方法可以帮助用户避免探索应该被认为是外推的预测。它还使用遗传算法在避免外推的约束因子区域上执行优化。在模拟和现实世界的例子中，我们演示了如何在分析器中没有约束的最佳因子设置经常外推，以及外推控制如何帮助避免这些解决方案与无效的因子设置，可能对用户没有用。
英文摘要：
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.
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