Classical ML, validation, metrics, interpretation, debugging · insight-1154

Compare a model gap with the seed-to-seed spread before declaring a winner

Situation
Gradient boosting or forest candidates differ by 0.003 in validation AUC and each was trained once with a single seed.
Action
Refit each candidate over several seeds and splits, report mean and spread, and require the gap to exceed that spread before choosing the more complex model.
Reason
Variance from data sampling, initialization and hyperparameter choices can be large enough to change benchmark conclusions.
Boundary
Close comparisons among tuned ensembles, feature-set ablations and library swaps.

Symptom or risk

Subsampling and feature sampling make each fit a random draw, so the observed gap can be smaller than the spread of one model across seeds.

Fully deterministic models, such as a regularized linear model with an exact solver, vary only by data split.

Several fits per candidate multiply compute; cap the budget and do it only for finalists.

Varying only the model seed and holding the split fixed, which hides split variance.

Variance from data sampling, parameter initialization and hyperparameter choice markedly affects machine-learning benchmark conclusions.
Accounting for Variance in Machine Learning Benchmarks