Machine learning techniques have been paramount throughout the last years, being applied in a wide range of tasks, such as classification, object recognition, person identification, and image segmentation. Nevertheless, conventional classification algorithms, e.g., Logistic Regression, Decision Trees, and Bayesian classifiers, might lack complexity and diversity, not suitable when dealing with real-world data. A recent graph-inspired classifier, known as the Optimum-Path Forest, has proven to be a state-of-the-art technique, comparable to Support Vector Machines and even surpassing it in some tasks. This paper proposes a Python-based Optimum-Path Forest framework, denoted as OPFython, where all of its functions and classes are based upon the original C language implementation. Additionally, as OPFython is a Python-based library, it provides a more friendly environment and a faster prototyping workspace than the C language.

翻译：过去几年来,机器学习技术一直处于至高无上的地位,在诸如分类、物体识别、人的身份识别和图像分割等广泛任务中应用这些技术。然而,传统的分类算法,例如后勤回归、决定树和贝叶西亚分类法,可能缺乏复杂性和多样性,在处理真实世界数据时并不合适。最近一个受图形启发的分类法,称为“最佳-和平森林 ” ( Optimum-Path Forest), 已被证明是一种最先进的技术, 与支持矢量机器相比,甚至在某些任务中超越了它。本文提出了一个以“OPFFython”为标志的基于“OPFython” 框架, 其所有功能和类别都基于原C语言的实施。此外, OPFFython是一个基于Python的图书馆,它提供了比C语言更友好的环境和更快的原型工作空间。