Relevance-based Prediction: A Transparent and Adaptive Alternative to Machine Learning

Abstract

The authors describe a new prediction system based on relevance, which gives a mathematically precise measure of the importance of an observation to forming a prediction, as well as fit, which measures a specific prediction’s reliability. They show how their relevance-based approach to prediction identifies the optimal combination of observations and predictive variables for any given prediction task, thereby presenting a unified alternative to both kernel regression and lasso regression, which they call CKT regression. They argue that their new prediction system addresses complexities that are beyond the capacity of linear regression analysis, but in a way that is more transparent, more flexible, and less arbitrary than widely used machine learning algorithms.