design-of-experiments

The 'design-of-experiments' skill provides expert guidance in designing experiments using Python. It solves the problem of selecting the right experimental design method by providing interactive, goal-driven recommendations based on user input. This skill helps users navigate through various experimental design strategies like classical DOE methods, Bayesian optimization, and model-driven designs, ensuring that the chosen approach aligns with the user's goals, constraints, and budget. By asking key questions, it tailors its suggestions to maximize the efficiency and information gain of each experiment.

Main features include an interactive decision tree that helps users select the most appropriate experimental design method based on factors like sequential vs. batch experiments, cost, number of factors, and prior knowledge. The skill supports classical methods such as full factorial designs and central composite designs, as well as advanced techniques like Bayesian optimization using Gaussian processes, active learning, and model-driven optimal designs. Users can easily choose between screening, optimization, and exploration strategies, with an emphasis on sequential or batch experimentation based on cost and complexity.

Target users include researchers, data scientists, and engineers involved in experimental design who need personalized recommendations for efficient and cost-effective experimentation. Whether the user is exploring new systems, optimizing existing processes, or screening for important factors, this skill offers tailored guidance that helps reduce trial and error and improves the quality of experimental outcomes.