In the field of machine learning, there exist effective models that achieve remarkable results. However, it is criticized from the perspective of the end user that many successful models behave like “black boxes.” In the desire for achieving excellent performances, the models become more complicated and less explainable. In this context, a major challenge has emerged: it is necessary to provide the intelligent models and algorithms with the property to be explained. In this article, a new paradigm is proposed: the minimalist machine learning. The conceptual cornerstone of the new paradigm is the assumption that it is possible to make the representation of the data of any problem of classification of patterns reduced to the Cartesian plane. The proposal is to design algorithms that are capable of achieving intelligent pattern classification in two dimensions (the plane) effectively, regardless of the number of attributes that the patterns contain.

中文翻译：

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走向黑匣子的漂白：极简机器学习

在机器学习领域，存在着取得显著成果的有效模型。然而，从最终用户的角度来看，许多成功的模型表现得像“黑匣子”。为了实现出色的性能，模型变得更加复杂且难以解释。在这种情况下，出现了一个重大挑战：必须为智能模型和算法提供要解释的属性。在本文中，提出了一种新范式：极简机器学习。新范式的概念基石是假设可以将任何模式分类问题的数据表示简化为笛卡尔平面。