Resumo:
Hyperparameter tuning is decisive for both predictive performance and computational cost in
machine learning models. In binary classification with Extreme Gradient Boosting, tuning is
inherently multiobjective: predictive quality must be maximized while execution time is
minimized. This work proposes a methodological framework that integrates Design of
Experiments, Response Surface Methodology, Factor Analysis, and the Normal Boundary
Intersection method to guide the selection of Extreme Gradient Boosting hyperparameters
under a fixed evaluation budget. The initial exploration is conducted through a fractional facecentered
central composite design, totaling 88 configurations. The observed responses
(accuracy, precision, recall, specificity, and runtime) are collected under a reproducible
protocol and summarized through factor scores obtained via principal component analysis with
Varimax rotation. These scores define quality and cost objective functions, reduce redundancy
among metrics, and support an interpretable assessment of trade-offs. Quadratic responsesurface
models are then fitted to the objective functions and used by Normal Boundary
Intersection to sample an approximately uniform Pareto frontier; candidate solutions are reevaluated
on the real model, and a final compromise configuration is selected. The proposed
approach is benchmarked, under equivalent evaluation budgets, against grid search, random
search, Bayesian optimization, and Hyperopt. Results show substantial computational savings:
the method achieves an average runtime of 0.078 s per fold and an average total runtime of
158 s, reducing total time by 9% to 71% relative to the benchmarks while maintaining high and
stable predictive performance across replications. As external validation, the proposed pipeline
was replicated on two additional datasets with contrasting profiles (balanced and highly
imbalanced), reproducing the cost–quality trade-off observed in the canonical benchmark and
reinforcing the multivariate stability of the method. Overall, integrating Design of Experiments
and Normal Boundary Intersection provides a parsimonious, interpretable, and replicable
alternative for multiobjective hyperparameter tuning, with potential applicability to other
Gradient Boosting Decision Tree families and cost-constrained settings.
