  The global optimization of classification trees has demonstrated considerable
promise, notably in enhancing accuracy, optimizing size, and thereby improving
human comprehensibility. While existing optimal classification trees
substantially enhance accuracy over greedy-based tree models like CART, they
still fall short when compared to the more complex black-box models, such as
random forests. To bridge this gap, we introduce a new mixed-integer
programming (MIP) formulation, grounded in multivariate Boolean rules, to
derive the optimal classification tree. Our methodology integrates both linear
metrics, including accuracy, balanced accuracy, and cost-sensitive cost, as
well as nonlinear metrics such as the F1-score. The approach is implemented in
an open-source Python package named BooleanOCT. We comprehensively benchmark
these methods on the 36 datasets from the UCI machine learning repository. The
proposed models demonstrate practical solvability on real-world datasets,
effectively handling sizes in the tens of thousands. Aiming to maximize
accuracy, this model achieves an average absolute improvement of 3.1\% and
1.5\% over random forests in small-scale and medium-sized datasets,
respectively. Experiments targeting various objectives, including balanced
accuracy, cost-sensitive cost, and F1-score, demonstrate the framework's wide
applicability and its superiority over contemporary state-of-the-art optimal
classification tree methods in small to medium-scale datasets.
