不依赖真实模型也能实现无偏机器学习预测
On design-unbiased algorithmic Machine Learning
- 基于抽样设计而非假设模型进行算法调优
- 在有限总体上实现预测无偏，无需知道真实数据分布
- 适合官方统计等需严格无偏性的场景
机器学习算法如kNN或随机森林通常以预测性能为导向，而非追求真实数据模型。然而，仅最小化均方误差（MSE）或F-score无法直接保证无偏性，而无偏性在官方统计等领域至关重要。本文研究在不依赖真实数据模型的前提下，如何通过合理采样训练集、调整算法参数，使预测或分类在给定有限总体上达到无偏性，并基于已知的抽样设计概率，实现对样本外性能的无偏评估。整个推断过程建立在已知的抽样设计基础上，而非假设分布或模型。
原文摘要 · Abstract (English)
Machine Learning (ML) algorithms, such as k-Nearest Neighbours (kNN) or random forest, eschew the ideal of true data models in favour of predictive performance. However, minimising the MSE or F-score cannot lead to unbiasedness directly, which is important in many situations such as official statistics. We study the conditions of algorithmic ML, other than the existence and knowledge of true data models, which lead to unbiased prediction or classification for a given finite population, including how the training data may be sampled from the population, how a trained prediction algorithm can be tuned to achieve unbiased prediction or classification for that population, and how the performance of out-of-sample prediction or classification can be assessed unbiasedly. The inference is based on the known probability design of samples and training sets, rather than any assumed distributions or models.
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