This paper proposes a methodological framework for applying machine learning techniques to domains characterized by weak, noisy, and poorly structured signals As a primary case study, we consider astrological prediction as a representative example of a weak-signal domain. Despite a long historical tradition and the existence of a large practitioner community, the field lacks systematic tools for empirical validation of its predictive claims. Existing studies tend to focus on isolated correlations rather than full predictive frameworks, leaving the central problem of hypothesis testing unresolved. To address this gap, we introduce a structured approach, enabling probabilistic modeling and signal detection under uncertainty. We argue that this approach provides a general pathway for evaluating predictive systems in domains where conventional validation methods are absent, and demonstrate its applicability through the formalization of input features and prediction tasks.
Aleksei Borealis (Mon,) studied this question.