Rapid advancements in genome sequencing have led to the collection of vast
amounts of genomics data. Researchers may be interested in using machine
learning models on such data to predict the pathogenicity or clinical
significance of a genetic mutation. However, many genetic datasets contain
imbalanced target variables that pose challenges to machine learning models:
observations are skewed/imbalanced in regression tasks or class-imbalanced in
classification tasks. Genetic datasets are also often high-cardinal and contain
skewed predictor variables, which poses further challenges. We aimed to
investigate the effects of data preprocessing, feature selection techniques,
and model selection on the performance of models trained on these datasets. We
measured performance with 5-fold cross-validation and compared averaged
r-squared and accuracy metrics across different combinations of techniques. We
found that outliers/skew in predictor or target variables did not pose a
challenge to regression models. We also found that class-imbalanced target
variables and skewed predictors had little to no impact on classification
performance. Random forest was the best model to use for imbalanced regression
tasks. While our study uses a genetic dataset as an example of a real-world
application, our findings can be generalized to any similar datasets.

通过研究数据的预处理、特征选择技术和模型选择对机器学习模型在基因数据集上的性能进行了调查，发现在回归任务中，异常值或偏斜的预测变量和目标变量对模型性能没有造成挑战；而分类任务中，类别不平衡的目标变量和偏斜的预测变量对性能几乎没有影响。随机森林是解决类别不平衡回归任务的最佳模型。尽管该研究以基因数据集为例，但其发现可推广到任何类似数据集。

不平衡遗传数据的改进分类和回归性能的数据预处理方法、特征选择技术和机器学习模型的比较分析

Comparative Analysis of Data Preprocessing Methods, Feature Selection  Techniques and Machine Learning Models for Improved Classification and  Regression Performance on Imbalanced Genetic Data