Battery performance datasets are typically non-normal and multicollinear. Extrapolating such datasets for model predictions needs attention to such characteristics. This study explores the impact of data normality in building machine learning models. In this work, tree-based regression models and multiple linear regressions models are each built from a highly skewed non-normal dataset with multicollinearity and compared. Several techniques are necessary, such as data transformation, to achieve a good multiple linear regression model with this dataset; the most useful techniques are discussed. With these techniques, the best multiple linear regression model achieved an R^2 = 81.23% and exhibited no multicollinearity effect for the dataset used in this study. Tree-based models perform better on this dataset, as they are non-parametric, capable of handling complex relationships among variables and not affected by multicollinearity. We show that bagging, in the use of Random Forests, reduces overfitting. Our best tree-based model achieved accuracy of R^2 = 97.73%. This study explains why tree-based regressions promise as a machine learning model for non-normally distributed, multicollinear data.

翻译：电池性能数据集通常是非正常的和多层的。 用于模型预测的外推这类数据集需要注意这些特性。 本研究探讨了数据正常度在建立机器学习模型中的影响。 在这项工作中, 树基回归模型和多线回归模型都是从高度偏斜的非正常数据集中建立的, 具有多线性和比较。 需要几种技术, 如数据转换等, 才能在这个数据集中实现良好的多线回归模型; 讨论最有用的技术。 有了这些技术, 最佳的多个线性回归模型就实现了 R ⁇ 2 = 81. 23%, 并且没有为本研究中使用的数据集显示多线性效应。 基于树的模型在这个数据集上表现更好, 因为它们是非参数模型, 能够处理变量之间的复杂关系, 不受多线性影响。 我们显示, 在使用随机森林时, 袋化会减少过度匹配。 我们的最佳树基模型实现了 R ⁇ 2 = 97.73 % 。 本研究解释了为什么基于树基的回归模型作为非正常分布的机器学习模型, 多线性数据的前景。