人类可交互地构建透明回归模型，通过调整顺序约束提升可解释性。
Human Guided Learning of Transparent Regression Models
- 将人类提供的顺序约束（如x<y）作为二值特征融入梯度提升回归模型
- 在9个数据集上，用户构建的模型在小数据集上表现优于基线，大样本下接近机器模型
- 适合需要模型可解释性的场景，如医疗、金融等需人工信任决策的领域
我们提出一种人机协同（HIL）的排列回归方法，即对给定物品排序预测连续值。模型采用梯度提升回归，将人类可理解的顺序约束（如x < y，表示物品x必须在y前）作为二值特征引入。该方法名为HuGuR（Human Guided Regression），允许用户交互式添加、移除和优化约束，同时系数实时计算。我们在用户研究中评估了HuGuR，对比其在9个数据集上的性能与多个基线方法。结果表明，用户构建的模型在小数据集上表现更优，总体表现与其它方法相当，且原则上对人类可解释。在同领域更大的数据集上，机器生成模型开始超越用户构建模型。未来工作将研究用户对自建模型的信任度，并探索该方法向字符串、序列、树或图等模式领域的迁移。
原文摘要 · Abstract (English)
We present a human-in-the-loop (HIL) approach to permutation regression, the novel task of predicting a continuous value for a given ordering of items. The model is a gradient boosted regression model that incorporates simple human-understandable constraints of the form x < y, i.e. item x has to be before item y, as binary features. The approach, HuGuR (Human Guided Regression), lets a human explore the search space of such transparent regression models. Interacting with HuGuR, users can add, remove, and refine order constraints interactively, while the coefficients are calculated on the fly. We evaluate HuGuR in a user study and compare the performance of user-built models with multiple baselines on 9 data sets. The results show that the user-built models outperform the compared methods on small data sets and in general perform on par with the other methods, while being in principle understandable for humans. On larger datasets from the same domain, machine-induced models begin to outperform the user-built models. Further work will study the trust users have in models when constructed by themselves and how the scheme can be transferred to other pattern domains, such as strings, sequences, trees, or graphs.
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