Parent: #478
The architecture audit inventories 491 estimator operations but has only 32 dynamic profile rows and many low/medium-confidence substrate labels. Static ownership labels are useful hypotheses, not enough evidence for optimization decisions.
Build automated profiling for the supported Flow-equivalent surface, capturing Python self-time, native/Cython/BLAS time, call crossings, allocations, cache/memory proxies where available, and operation phase. Prioritize all canonical operations, then all implemented estimators.
Acceptance: 100% dynamic profile coverage for canonical operations; confidence upgraded from low/medium where measured; profile artifacts keyed to sklearn version and hardware; optimization roadmap uses measured substrate when available and never labels already-equivalent as a performance conclusion.
Parent: #478
The architecture audit inventories 491 estimator operations but has only 32 dynamic profile rows and many low/medium-confidence substrate labels. Static ownership labels are useful hypotheses, not enough evidence for optimization decisions.
Build automated profiling for the supported Flow-equivalent surface, capturing Python self-time, native/Cython/BLAS time, call crossings, allocations, cache/memory proxies where available, and operation phase. Prioritize all canonical operations, then all implemented estimators.
Acceptance: 100% dynamic profile coverage for canonical operations; confidence upgraded from low/medium where measured; profile artifacts keyed to sklearn version and hardware; optimization roadmap uses measured substrate when available and never labels
already-equivalentas a performance conclusion.