minimumazetoop
Minimumazetoop is a theoretical construct in optimization and machine learning that describes the minimal feature set or parameterization required to achieve a specified performance target in a model, balancing accuracy and complexity.
The term was coined to express the idea of obtaining the simplest model that retains predictive power.
Practically, minimumazetoop is identified through iterative feature pruning, sparse regularization, and cross-validated evaluation. The objective is
Applications include model compression for edge devices, interpretable AI, and resource-constrained decision systems. It provides a
Limitations include sensitivity to data distribution and threshold choices, potential instability in feature selection across datasets,
See also: feature selection, model compression, Pareto efficiency.