design-of-experiments

Classical designs like factorials and Box-Behnken suit screening and fixed budgets; Bayesian optimization adapts sequentially to maximize information per run. Choose classical DOE for predetermined factor ranges and Bayesian when you can run experiments iteratively and refine objectives.

Fractional factorial designs efficiently screen factors in fewer runs than full factorials by confounding high-order interactions. This Skill recommends appropriate fractional designs based on your factor count and run budget.

Yes. Gaussian-process-based Bayesian optimization adaptively selects next experiments by balancing exploration and exploitation, reducing total runs needed. This Skill integrates scikit-optimize and GPy for this workflow.

DOE analysis compares factor effects and model fit to guide next steps. This Skill provides decision-tree guidance to recommend screening, optimization, model discrimination, or robustness tasks based on your findings.

Yes. Active learning via modAL selects experiments that maximize information gain about uncertain regions. This Skill integrates active learning for model-driven designs when sequential experimentation is feasible.

Define your experimental question, list factors and ranges, set run budget, and choose design goal—screening, optimization, or robustness. This Skill guides you through a question-driven flow to select the right design method.