In this paper, I will introduce a new form of regression, that can adjust overfitting and underfitting through, "distance-based regression." Overfitting often results in finding false patterns causing inaccurate results, so by having a new approach that minimizes overfitting, more accurate predictions can be derived. Then I will proceed with a test of my regression form and show additional ways to optimize the regression. Finally, I will apply my new technique to a specific data set to demonstrate its practical value.

翻译：本文提出一种新的回归形式，即通过"基于距离的回归"来调整过拟合与欠拟合现象。过拟合常导致发现虚假模式并产生不准确结果，因此采用这种能最小化过拟合的新方法可获得更精确的预测。随后将对本回归形式进行测试，并展示优化该回归的补充方法。最后将把新技术应用于特定数据集以验证其实际价值。