MLsidoksia

MLsidoksia is a term used in discussions of machine learning to describe the network of dependencies that emerges when predictive models interact with data representations, training procedures, and evaluation criteria. It is not a formal standard, but a conceptual lens for analyzing how choices at different levels of a machine learning pipeline influence outcomes and robustness.

The concept draws on ideas from causal inference, information theory, and graphical models, treating models, features, and datasets as nodes in a dependency structure.

Techniques associated with MLsidoksia include graph-based modeling of dependencies, regularization schemes that promote or suppress connections between features and predictions, and diagnostic tools that trace the impact of data edits on model outputs.

Applications include model interpretability studies, data provenance and auditing, and robustness analysis under distributional shift. It is particularly relevant when comparing alternative architectures or preprocessing pipelines.

Limitations include the lack of a universally accepted definition, potential ambiguity in what constitutes a meaningful dependency, and the risk of over-interpretation. It is typically used heuristically rather than as a formal methodology.

See also: causality in machine learning, interpretable ML, data provenance, dependency graphs.