Evaluation & synthesis
Stability analysis
Test how sensitive an analytical result is to controlled changes in the data or analytical conditions.
Overview
Stability analysis examines whether an analytical result remains similar when the conditions that produced it are changed in controlled ways.
This provides a complementary view to ordinary performance or separation metrics by testing sensitivity rather than evaluating only the original run.
Perturbation stability
The available stability mode slightly perturbs numerical observations and measures how consistently the analytical result is recovered.
- Run-level stability metrics
- Per-cluster stability information where applicable
- Contribution to the Stability evaluation pillar
Other stability modes (planned)
These modes are not currently available and should not be confused with the existing perturbation analysis.
- Planned — Algorithmic stability (planned): Tests sensitivity to algorithmic randomness, such as alternative model initializations. [Randomness, Initialization]
- Planned — Noise stability (planned): Tests whether irrelevant or deliberately noisy features materially alter the result. [Noise, Feature sensitivity]
- Planned — Subsampling stability (planned): Measures whether structure persists when analyses are repeated on subsets of observations. [Subsampling, Robustness]
Interpretation and limitations
- High stability does not by itself imply that an analytical structure is meaningful.
- Different stability tests probe different failure modes and should not be treated as interchangeable.
- Sensitivity may reflect properties of the dataset, preprocessing, model, parameters, or representation.