论文标题
循环增强 - 可解释的监督机器学习算法
Cyclic Boosting -- an explainable supervised machine learning algorithm
论文作者
论文摘要
监督的机器学习算法在广泛的特定应用中取得了壮观的进步，并超过了人类水平的表现。但是，使用复杂的合奏或深度学习算法通常会导致黑匣子模型，在这种模型中，导致个人预测的路径无法详细遵循。为了解决此问题，我们提出了小说的“循环增强”机器学习算法，该算法允许有效执行准确的回归和分类任务，同时允许对每个单个预测的做出详细了解。
Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep learning algorithms typically results in black box models, where the path leading to individual predictions cannot be followed in detail. In order to address this issue, we propose the novel "Cyclic Boosting" machine learning algorithm, which allows to efficiently perform accurate regression and classification tasks while at the same time allowing a detailed understanding of how each individual prediction was made.
