提出新方法解决置信预测的精度、效率和形状控制难题
On some practical challenges of conformal prediction
- 设计二次多项式非符合度度量，提升预测区域确定性
- 在保证覆盖率的前提下，显著降低计算开销并控制区域形状
- 适合需要高可靠性预测的工业场景或安全敏感应用
置信预测是一种无需依赖模型的机器学习方法，可在保证覆盖概率的前提下构建预测区间。然而，数据科学家在实际应用中常面临三大挑战：(i) 预测区间的确定仅是近似值，威胁有限样本下的有效性；(ii) 计算成本可能极高；(iii) 预测区域的形状难以控制。本文深入探讨了非符合度度量单调性与可置信度函数单调性之间的关系，基于新发现提出一种二次多项式非符合度度量，可在完整置信预测框架内同时克服上述三个挑战。
原文摘要 · Abstract (English)
Conformal prediction is a model-free machine learning method for constructing prediction regions at a guaranteed coverage probability level. However, a data scientist often faces three challenges in practice: (i) the determination of a conformal prediction region is only approximate, jeopardizing the finite-sample validity of prediction, (ii) the computation required could be prohibitively expensive, and (iii) the shape of a conformal prediction region is hard to control. This article offers new insights into the relationship among the monotonicity of the non-conformity measure, the monotonicity of the plausibility function, and the exact determination of a conformal prediction region. Based on these new insights, we propose a quadratic-polynomial non-conformity measure that allows a data scientist to circumvent the three challenges simultaneously within the full conformal prediction framework.
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