Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of wellknown machine learning algorithms have been developed for use in the field of computer science to analyze data. This paper introduced a new machine learning algorithm called impact learning. Impact learning is a supervised learning algorithm that can be consolidated in both classification and regression problems. It can furthermore manifest its superiority in analyzing competitive data. This algorithm is remarkable for learning from the competitive situation and the competition comes from the effects of autonomous features. It is prepared by the impacts of the highlights from the intrinsic rate of natural increase (RNI). We, moreover, manifest the prevalence of the impact learning over the conventional machine learning algorithm.

翻译：机器学习是计算机算法的研究,这种算法可以根据数据和经验自动改进。机器学习算法从抽样数据(称为培训数据)中建立模型,在没有明确编程的情况下作出预测或判断。各种众所周知的机器学习算法已经开发出来,用于计算机科学领域分析数据。本文介绍了一种新的机器学习算法,称为影响学习。影响学习是一种监督的学习算法,可以同时在分类和回归问题中加以整合。还可以在分析竞争性数据方面显示出其优越性。这种算法对于从竞争情况和竞争中学习是引人注目的,而竞争则来自自主特性的影响。它是根据自然增长内在速率(RNI)的亮点的影响编写的。此外,我们还显示了在常规机器学习算法上影响学习的普及程度。