  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.
