An Intuitive Guide to Relevance-Based Prediction
提出一种结合马氏距离和香农信息论的新预测方法，通过相关性、拟合度和共依赖三个核心概念，为每个预测任务联合选择观测和变量，作为线性回归和机器学习的替代方案。
Relevance-based prediction is a new approach to data-driven forecasting that serves as a favorable alternative to both linear regression analysis and machine learning. It follows from two seminal scientific innovations: Prasanta Mahalanobis’ distance measure and Claude Shannon’s information theory. Relevance-based prediction rests on three key tenets: 1) <italic>relevance</italic>, which measures the importance of an observation to a prediction; 2) <italic>fit</italic>, which measures the reliability of each individual prediction task; and 3) <italic>codependence</italic>, which holds that the choice of observations and predictive variables should be determined jointly for each individual prediction task.