Ensemble and Tuning Method Comparisons Often Measure Luck, Not Performance

A developer writing six posts on ensemble and hyperparameter tuning methods discovered that nearly every comparison produced gains smaller than the random variation caused by changing a single seed. For example, grid search versus random search showed a performance gap of just 0.0008, while the spread across random seeds for the same method reached 0.0098, meaning a single-seed experiment had roughly a coin-flip chance of favoring either approach. Bayesian optimization was the one exception, showing a consistently growing advantage with more trials and far lower variance across seeds than random search. Improper stacking — training the combiner on in-fold predictions — not only inflated scores but actively selected the wrong underlying model. The author concludes that most published comparisons of these techniques are effectively reporting luck attributed to a method's name rather than a genuine performance difference.