Unsupervised Model Selection

Unsupervised model selection addresses the challenge of choosing the best machine learning model for a given task without labeled data, a critical bottleneck in many applications like anomaly detection and domain adaptation. Current research focuses on developing surrogate metrics to evaluate model performance in the absence of ground truth, exploring techniques like robust rank aggregation and nested cross-validation, and investigating the use of pre-trained foundation models for zero-shot prediction. Improved unsupervised model selection methods are crucial for enhancing the reliability and reproducibility of unsupervised machine learning across diverse fields, from manufacturing quality control to healthcare.