  A graph-inspired classifier, known as Optimum-Path Forest (OPF), has proven
to be a state-of-the-art algorithm comparable to Logistic Regressors, Support
Vector Machines in a wide variety of tasks. Recently, its Python-based version,
denoted as OPFython, has been proposed to provide a more friendly framework and
a faster prototyping environment. Nevertheless, Python-based algorithms are
slower than their counterpart C-based algorithms, impacting their performance
when confronted with large amounts of data. Therefore, this paper proposed a
simple yet highly efficient speed up using the Numba package, which accelerates
Numpy-based calculations and attempts to increase the algorithm's overall
performance. Experimental results showed that the proposed approach achieved
better results than the na\"ive Python-based OPF and speeded up its distance
measurement calculation.

Machine Learning;Artificial Intelligence